=Paper=
{{Paper
|id=Vol-3892/paper10
|storemode=property
|title=Web-Based Melanoma Detection System Using Convolutional Neural Networks and Advanced Image Processing
|pdfUrl=https://ceur-ws.org/Vol-3892/paper10.pdf
|volume=Vol-3892
|authors=Sebastian Górecki,Wiktoria Duszczyk,Zuzanna Huda,Andrzej Faryna,Aleksandra Tatka,Mohamed Adel,Mohamed Aborizka,Yurii Oliinyk,Mariia Kapshuk,Leonid Oliinyk,Inna Rozlomii,Andrii Yarmilko,Serhii Naumenko
|dblpUrl=https://dblp.org/rec/conf/iddm/GoreckiDHFT24
}}
==Web-Based Melanoma Detection System Using Convolutional Neural Networks and Advanced Image Processing==
https://ceur-ws.org/Vol-3892/paper10.pdf
Web-based melanoma detection system using convolutional
neural networks and advanced image processing⋆
Sebastian Górecki1*,†, Wiktoria Duszczyk1,† Zuzanna Huda1,†, Andrzej Faryna2,† and
Aleksandra Tatka2,†
1
Maria Sklodowska-Curie Warsaw Higher School, Al. Solidarności 12, 03-411 Warszawa
2
Centrum Diagnostyczne im. Marii Skłodowskiej-Curie sp. z o.o., Jasionka 954, 36-002 Jasionka, Polska
Abstract
Melanoma, an aggressive malignancy of melanocytes, is one of the deadliest skin cancers, marked by high
mortality rates, particularly when diagnosed late. This study presents the development of an autonomous
diagnostic system designed to detect melanoma using embedded AI technologies, specifically
convolutional neural networks and advanced image processing methods. The system aims to enhance
diagnostic accuracy, shorten waiting times for diagnosis, and provide a non-invasive, accessible solution
for early melanoma detection. We leverage deep learning models trained on a diverse dataset of
dermoscopic images, combined with innovative pre-processing and segmentation techniques, to achieve
high-performance melanoma classification. The results demonstrate the potential of this web-based
system to serve as an effective decision support tool for clinicians, ultimately improving patient outcomes
through early intervention.
Keywords
Melanoma, Autonomous Diagnostic Systems, Convolutional Neural Networks, AI in Dermatology
1. Introduction
Skin melanoma is one of the most common malignancies, with its incidence increasing
significantly over the past few decades, making it a serious public health concern. In Western
populations, one in 50 individuals is expected to develop melanoma. While most cases occur in the
elderly, melanoma is also the third most common cancer in adolescents and young adults aged 15
to 39 years [1].
The development of melanoma is multifactorial, involving an interplay between genetic
susceptibility and environmental factors. Primary melanomas can present with a wide range of
pigmentation, from heavily pigmented to amelanotic. Early diagnosis of melanoma is a crucial
factor in improving patient survival rates.
The traditional approach to melanoma diagnosis begins with a visual examination, followed by
biopsy and histopathological evaluation. The major challenge in melanoma detection lies in
accurately identifying early-stage melanomas while minimizing the need for biopsies of benign
lesions.
Recent advancements in noninvasive diagnostic techniques have enhanced the accuracy of
melanoma detection, particularly in managing melanocytic lesions with uncertain diagnoses.
Additionally, the promising potential of artificial intelligence offers a transformative opportunity
in the field of melanoma detection.
IDDM’24: 7th International Conference on Informatics & Data-Driven Medicine, November 14 - 16, 2024, Birmingham, UK
∗
Corresponding author.
†
These authors contributed equally.
sebastian.gorecki@dokt.p.lodz.pl (S. Górecki); wiktoria.duszczyk@adres.pl (W. Duszczyk); zuziahuda@gmail.com (Z.
Huda); andrzejfaryna@gmail.com (A. Faryna); olatatka@gmail.com (A. Tatka)
0000-0001-5700-4000 (S. Górecki); 0009-0005-3322-1904 (W. Duszczyk); 0009-0009-2481-8281 (Z. Huda), 0009-0004-
7388-2603 (A. Faryna); 0000-0002-8268-894X (A. Tatka)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Skin cancer is the most common malignancy affecting humans [2]. Early recognition is the most
effective intervention to improve melanoma prognosis [3]. Both melanoma and non-melanoma
skin cancers are showing a gradually increasing incidence worldwide, especially in the Caucasian
population, posing a growing health problem due to the associated morbidity, mortality, and
economic burden of monitoring and treatment.
Melanoma, a malignant skin cancer originating from melanocytes, poses a significant public
health threat due to its rapid progression and high metastatic potential. In Poland, melanoma
accounts for approximately 2% of all cancer deaths, and its incidence has been increasing by 5-6%
annually. The implementation of AI-powered autonomous diagnostic systems can play a crucial
role in addressing the challenges of early melanoma detection and improving patient outcomes.
2. Related works
This section describes current diagnostic methods for detecting skin cancer. While some of these
techniques were developed decades ago, they have undergone continuous transformation and
improvement. We will focus on methods such as dermoscopy, digital photography, and
multispectral imaging, which provide better visualization of skin lesions. However, these
approaches often require expert interpretation by experienced dermatologists and can be time-
consuming. Advances in artificial intelligence, particularly the rapid development and deployment
of convolutional neural networks, have enabled the automatic classification of skin images, greatly
improving the accuracy and efficiency of cancer detection.
Figure 1: Melanoma detection technology development timeline [4]
The figure 1. illustrates the evolution of melanoma detection technologies over the years,
reflecting key advancements that have shaped the field of skin cancer diagnostics. Early methods,
such as dermoscopy, provided the foundational tools for visual inspection, whereas digital and
multispectral imaging later emerged, enhancing lesion visualization and pattern recognition. More
recent advancements in artificial intelligence (AI), specifically convolutional neural networks
(CNNs), have accelerated the capacity for automatic lesion classification, improving diagnostic
accuracy and efficiency. These innovations highlight the ongoing transition toward AI-driven
methods that offer substantial improvements over traditional diagnostic approaches.
2.1. ABCDE Principle
Measuring characteristics from an image, such as color, texture, or shape helps distinguish benign
from malignant melanoma. In this work, the ABCD rule is used which is divided into four phases:
Asymmetry, Border, Color, and Diameter. In 2004, it was expanded to E –Evolving to reflect the
possibility of melanoma in rapidly progressing moles. This is commonly applied by physicians,
healthcare workers, or even patients to determine skin lesions for melanoma or features
concerning. The tool was developed to provide a simple and straightforward template that
laypersons and physicians could follow in case of features that may represent melanoma.
(a) (b) (c) (d) (e)
Figure 2: ABCDE characteristics of melanoma [5]
Asymmetry (Figure 2.a) – is a significant indicator evaluated by splitting the image into
two parts and comparing them. It is benign if both pieces are identical; if they are
asymmetrical on both axes (x,y), it is melanoma.
Border (Figure 2.b) - to determine this score, the lesion is divided into eight octants. Every
slice has a score of one for an irregular perimeter and zero for a regular one. As a result,
zero is the lowest border score and eight is the highest.
Color (Figure 2.c) – this feature is considered up to the presence of six following colours:
white, red, light or dark brown, blue-grey, and black. The score is increased by the
presence of each color that is present therefore, the range is from 0 - 6.
Diameter (Figure 2.d) – characteristic presents an alteration in the size of lesions which is
usually increased. Melanomas are frequently larger than 6 milliammeters.
Evolving (Figure 2.e) – indicator shows if there are lesions that look different from
previous ones or if there are present changes in shape, color, or texture.
The ABCD criteria [5] have great profits for physicians and healthcare workers who don’t have
enough experience in screening and diagnostic methods to examine skin lesions. Moreover, the
introduction of the ABCDE rule improved patient education about melanoma and self-skin
examination. However, the ABCDE rule doesn’t include all melanoma features, and sometimes
melanoma moles don’t present characteristics aided by ABCDE criteria. In studies, Abbasi NR et al.
and Thomas L et al. showed a sensitivity and a specificity of the differentiating of benign from
malignant based on ABCDE principles have the following results: 57 – 100 % and 37 – 100%. One of
the other limitations is insufficient knowledge of laypeople who don’t understand how to refer a
lesion to the ABCDE rule therefore, there is necessary cognitive training to educate patients and
improve their accuracy of detection of skin cancer.
2.2. Dermoscopy
The advancement in skin cancer diagnosis has been facilitated using a dermoscope – a simple,
handheld optical device. The dermoscope provides 10x magnification and uses illumination that
minimizes surface light reflection. This technique allows for a more precise visualization of the
structures and pigmentation beneath the stratum corneum, which are typically not visible to the
naked eye. [6] Dermoscopy is a noninvasive technique that enables the clinician to perform direct
microscopic examination of diagnostic features, not seen by the naked eye, in pigmented skin
lesions. The device operates by positioning it directly on or near the skin while the built-in light
source is activated, allowing the user to examine the targeted lesion through the magnifying lens
[7]. In the meta-analysis conducted for the National Institute for Health Research (NIHR) Cochrane
Systematic Reviews Program the findings indicate that dermoscopy is more effective than visual
inspection alone in both accurately diagnosing melanoma and ruling out conditions that are not
melanoma [8].
Melanoma can be difficult to distinguish not only clinically but also dermoscopically from
melanocytic nevi, especially in early-stage lesions where specific malignant features may be absent
[9]. In addition, dermatoscopic structures and patterns used in the detection of melanoma. There
are many helpful algorithms, including the ABCD rule, the Menzies method, the 7-point checklist,
the 3-point checklist, chaos and clues, and CASH (color, architecture, symmetry, and homogeneity)
[10]. The results of this systematic review and meta-analysis highlight the diagnostic relevance of
dermoscopic features linked to melanoma detection, such as shiny white structures and the blue-
white veil. They further emphasize the importance of recognizing the overall pattern and may
indicate a hierarchy regarding the significance of these features and patterns [11].
Based on the principle that benign lesions tend to remain stable, while melanoma typically
changes over time, digital follow-up of melanocytic lesions has been proposed as a strategy to
identify melanomas that may lack clear dermoscopic features at the initial assessment. Whole-body
photography (TBP) has been widely used for patients with extensive or atypical nevi, identifying
malignant lesions can be challenging TBP involves capturing high-resolution, full-body clinical
images as an adjunct to total body skin examinations (TBSE) during follow-up visits. This approach
helps in identifying new or changing lesions and provides reassurance to both the patient and
physician by showing the stability of lesions over time. TBP is particularly beneficial for patients
with extensive or atypical nevi [12]. Dermatoscopic examination, although a very useful tool in
diagnosing skin lesions, has certain limitations such as the necessity of having a dermatoscope, the
subjectivity of assessment depending on knowledge and experience, and limited capability in
detecting deeper lesions.
2.3. Ultrasonography (USG)
Diagnosis of skin melanoma using ultrasound is based on several main aspects. This method is
used to assess the advancement and progression of the disease. It is mainly important in cases of
suspected metastases to lymph nodes or other organs. Ultrasonography is a diagnostic technique
using high-frequency sound waves. The ultrasound machine records reflected sound waves from
given structures, depending on the density of the examined area and the structures located there.
The reflected waves are converted into a computer image in real time, which allows for the study
of the dynamics of processes in the body [13]. This is a common diagnostic test, but in the case of
skin melanoma it will be limiting. It complements the medical diagnosis of skin melanoma and will
be important for specific purposes:
To examin regional lymph nodes, in case of diagnosing possible metastases. It is an
important aspect in advanced stages of melanoma. The ultrasound of lymph nodes allows
to asses their size, shape and echogenicity (in the case of metastases they will be enlarged
and have a pathological shape).
An estimation of Breslow depth, the depth of infiltration of the lesion in the skin. This
parameter is difficult to determine clinically, and the ultrasound examination helps in its
assessment. The analysis of tumor thickness, is essential in predicting outcomes and
guiding treatment for invasive melanomas. This metric has been integrated into the staging
frameworks developed by the American Joint Committee on Cancer, helping to define
pathological stages, set appropriate excision margins, and indicate when a sentinel lymph
node biopsy should be performed [15].
An assessment of metastases to internal organs, is important examination in case of later
stages of the disease and its diagnosis. Analysis of possible metastases in organs such as
liver etc.
Monitoring the body's response to tailored treatment. Re-diagnosis of lymph nodes or
tumors in the case of implementation of appropriate treatment.
The use of ultrasound in the diagnosis of skin melanoma is important in case of its advanced
stage. Studies suggest that it is necessary to observe, control and monitoring changes in the lymph
nodes and metastases to other organs. The ability to capture real-time images, measure the
morphological and physiological characteristics of the skin, along with the safety of using non-
ionizing media and the absence of contraindications, are additional benefits of skin sonography.
However, ultrasound has many disadvantages and significant limitations. It is not a test that allows
for quick diagnosis and spontaneous diagnosis of early stages of skin melanoma. It is not able to
identify characteristic features of superficial skin lesions. It is limiting in the case of early diagnosis
of neoplasms and is a complementary examination, especially in the case of later stages of skin
cancer. In addition, the described diagnostics are subject to limitations due to the possible lack of
experience of the operator performing the test [14] [15]
2.4. AI-Driven Innovations in Early Melanoma Detection
The application of artificial intelligence (AI), particularly convolutional neural networks (CNNs), in
medical image analysis has revolutionized the field of dermatology. CNNs have demonstrated
extraordinary performance in the classification of skin lesions, consistently surpassing traditional
image analysis techniques in terms of accuracy and reliability. These deep learning models have an
unparalleled capacity for extracting critical features from dermoscopic images, enabling the
differentiation between benign and malignant lesions with exceptional precision. One study has
reported an area under the receiver operating characteristic curve (AUC) of 0.94 for a CNN model
in detecting melanoma, highlighting its potential as a decision support tool in clinical settings [16].
AI models, particularly CNNs, excel in feature extraction due to their multi-layered architecture,
which enables automatic identification of hierarchical patterns within images. These models are
capable of discerning minute variations in skin lesion characteristics, such as texture, shape, and
color, which are often difficult for the human eye to detect. Such capabilities significantly enhance
diagnostic accuracy, especially in the early stages of melanoma, where early intervention can
drastically improve patient outcomes.
In addition to standard CNN architectures, transfer learning has emerged as a powerful
technique to mitigate the challenges associated with limited medical datasets. Transfer learning
allows for the utilization of pre-trained models often trained on large general datasets, such as
ImageNet—and fine-tuning them on domain-specific data, such as dermoscopic images of skin
lesions. Leveraging transfer learning have reported significant improvements in classification
accuracy, even when training data is limited. This approach not only reduces the computational
burden but also accelerates the development of highly accurate diagnostic models, making them
accessible for clinical use in under-resourced settings.
Despite the considerable advancements in AI-driven skin lesion detection, several limitations
persist that impede the widespread clinical adoption of these models. One of the foremost
challenges is the reliance on large, annotated datasets for training. Although datasets such as ISIC
and HAM10000 have been instrumental in advancing research, the scarcity of diverse and
representative datasets remains a significant hurdle. For instance, most publicly available datasets
are skewed towards specific skin types, ethnicities, and geographic regions, which can lead to
biased model predictions when applied to underrepresented populations [17]. Addressing this issue
requires not only the expansion of existing datasets but also the inclusion of more diverse skin
phototypes, lesion types, and patient demographics.
Moreover, the inherent variability in lesion presentation, including differences in lesion size,
shape, and color across different stages of melanoma, poses additional challenges. CNN models,
while highly effective at identifying well-represented lesion types, may struggle to generalize
across varied presentations of atypical or rare skin lesions. This variability can result in
misclassifications, particularly in cases where early-stage melanomas exhibit subtle features that
are difficult to distinguish from benign lesions. Furthermore, certain dermoscopic features that are
vital for melanoma diagnosis, such as the presence of regression structures or blue-white veils, may
not be adequately captured in the training data, further complicating the classification process.
Another significant concern is the black-box nature of CNNs. While these models provide high
accuracy, their decision-making process is often opaque, making it difficult for clinicians to
interpret the rationale behind a given prediction. This lack of transparency has led to calls for more
interpretable AI models that can offer insights into how specific features influence diagnostic
outcomes. Techniques such as Grad-CAM (Gradient-weighted Class Activation Mapping) and
SHAP (SHapley Additive exPlanations) have been proposed to enhance model interpretability by
visualizing the regions of the image that most contributed to the model’s decision. However,
further research is needed to make these techniques more clinically useful.
2.5. Histopathological Diagnosis of Melanoma
Histopathological examination remains the gold standard in melanoma diagnosis, confirming
malignancy through microscopic evaluation of biopsied tissue. Pathologists employ specialized
staining techniques such as HMB-45, S-100, and MART-1 to distinguish malignant melanocytes
from benign lesions. The tissue, once biopsied, is fixed, embedded in paraffin, sectioned, and
stained for microscopic evaluation. Key histopathological features like irregular lesion borders,
nuclear atypia, and mitotic activity are assessed to determine malignancy and stage. Upon
histopathological confirmation, treatment is guided by crucial factors like Breslow thickness -
depth of tumor invasion and Clark level -extent of penetration into skin layers. For instance, a
Breslow thickness exceeding 1 mm typically necessitates wide excision of the lesion. When the
thickness exceeds 4 mm, more aggressive treatment may be warranted, including adjuvant
therapies like PD-1 or BRAF inhibitors. Additional features such as ulceration or lymphatic
invasion signal a poorer prognosis and can prompt more intensive treatment plans. Histopathology
plays a pivotal role in determining eligibility for immunotherapies. Molecular and histological
characteristics identified during evaluation assist clinicians in tailoring personalized treatment
plans, optimizing patient outcomes [18]. Despite its diagnostic accuracy, histopathological
diagnosis has certain limitations. The process can be time-consuming, with more complex cases
requiring several days to weeks for conclusive results, potentially delaying treatment initiation.
Costs can also be a significant barrier, especially when advanced analyses like BRAF mutation
testing are involved. These expenses cover not only the biopsy and immunohistochemical staining
but also specialist consultations, which may be limited in certain medical centers. The availability
of such diagnostics varies globally, with some regions facing delays and higher costs due to limited
access to specialized laboratories. Furthermore, histopathological analysis, while reliable, is not
infallible, with potential for misinterpretation in ambiguous cases, leading to false-negative or
false-positive results [19].
3. Project Objective
The goal of this project is to develop an advanced, autonomous diagnostic system for the early
detection of melanoma using convolutional neural networks (CNNs) and sophisticated image
processing techniques. This system is designed to reduce diagnostic waiting times by
approximately 35 minutes and improve detection accuracy by 20% compared to traditional
diagnostic methods. The key objectives are:
Development of an autonomous diagnostic system: A system capable of analyzing
dermoscopic images to diagnose melanoma autonomously.
Efficiency and accuracy: The system will enhance diagnostic precision while reducing
the time needed for professional evaluation.
Accessibility: It will be accessible to a wide range of users, enabling early detection
through intuitive, easy-to-use mobile platforms.
The scope of work for this project includes:
1. Development of the Diagnostic Algorithm:
o The core of the system will be based on convolutional neural networks specifically
trained to detect melanoma in dermoscopic images.
o The CNN models will be developed and optimized to achieve high accuracy in
recognizing and classifying skin lesions as benign or malignant.
2. Mobile and Web Platform Integration:
o The diagnostic system will be integrated into an accessible mobile and web-based
platform.
o This platform will allow users to easily upload dermoscopic images for analysis.
o The system's user interface will be designed for simplicity and ease of use,
ensuring that individuals without medical expertise can effectively navigate the
application.
3. System Validation and Testing:
o These tests will focus on measuring the accuracy, specificity, and sensitivity of the
system in correctly identifying melanoma.
3.1. Expected Outcomes
The proposed project aims to develop an autonomous diagnostic system for the early detection of
melanoma using convolutional neural networks and advanced image processing techniques. This
system is designed to analyse dermoscopic images and automatically classify skin lesions as
malignant or benign, with a target accuracy over 60%, significantly improving upon conventional
diagnostic methods.
Furthermore, the project seeks to raise public awareness about the importance of early detection
and diagnosis of skin cancer. The system will incorporate educational features to inform users
about the risks associated with neglecting suspicious skin lesions, as early recognition of
melanoma is crucial for improving treatment outcomes and patient prognosis.
The application will be designed to be user-friendly and accessible, allowing individuals with
varying levels of technical expertise to utilize the system. By integrating the diagnostic system into
a mobile and web-based platform, the project aims to make it widely accessible, as users with a
smartphone equipped with a camera and internet access can easily upload and analyse their skin
lesion images.
To address potential variability in image quality, the system will employ advanced image
processing algorithms to effectively analyze even suboptimal images. In cases where the image
quality does not meet the required standards for accurate diagnosis, the system will provide
feedback to the user, prompting them to retake the image to ensure the highest possible diagnostic
accuracy.
3.2. Key points of projects
The accuracy and broad diagnostic base of the proposed method will significantly speed up
the process of mole recognition. Accelerating diagnosis is crucial in implementing possible
treatment, which, due to the low stage of the cancer, will be less burdensome to the body.
Nowadays, more and more people have access to smartphones, so with the knowledge of
the existence of the proposed application, many owners of the necessary equipment will
check their signs. It will also be a form of promoting the examination of skin lesions.
A mobile application installed on a smartphone will reduce the burden on potential users.
To use it, you do not have to leave your home, and you can check moles without time
limits. Thanks to this, when the patient speculates about the diagnosis, the proposed
method can have a positive impact on the patient-user's psyche.
The project does not exclude any user. Regardless of the quality of the supported camera,
the application can analyse the photo.
Reduction in the number of biopsies. Even low-risk routine surgical procedures are
associated with morbidity, mounting health care costs, and patient anxiety.
3.3. Add values
Reducing the time of doctor’s working by mobile application, may decrease the time of
doctors working on identifying and diagnosing skin lesions in melanoma by examining
skin lesions and providing preliminary diagnosis. Moreover, it may help identify whether
the mole is benign, malignant, or requires further investigation. This optimizes the doctor's
workflow and supports their decisions.
Remote diagnosis by smartphone application provides health care services for people who
live in less urbanized places or underserved areas. It reduces costs related to travelling to
clinic appointments, and day off work but also costs with diagnostic tools. The next
advantage is decreased exposure to infectious diseases on which patients might be exposed
in the clinic.
Introducing a novel non-invasive diagnosis method leads to several benefits in melanoma
diagnosis which can improve the patient’s life standard and the effectiveness of diagnosis.
It opens perspective in skin cancer diagnosis to revolutionize current technologies and
optimize the recognition process of skin moles.
3.4. Limitation
The dataset applied for the recognition of skin lesions is limited in the diversity of lesions
and doesn’t include all significant data to accurately diagnose each case. An expanding
dataset is required to provide errorless patient outcomes.
The application analyses visual features of skin lesions such as color, border, shape, and
other characteristics seen by the eye. It doesn’t consider molecular aspects such as cell
morphology, tumor invasion in a tissue, and structural disorganization in skin tissue.
Therefore, it doesn't replace histology techniques, which are more specific in examining
melanoma stages.
Misdiagnosis of a skin lesion due to incorrect date recording or technical issues may affect
decision-making for laypersons and health care workers and lead to plenty of
consequences in the recognition process of a skin mole.
The lack of universal standards for mobile applications and concerns about the security of
patient's medical records have been challenging and require licensing laws.
Insufficient education of laypeople and lack of understanding limitations of the tool are
potential risks to misinterpretation of results and non-comprehensive investigation due to
replacement of dermatologist appointments.
4. Methodology
The development of the autonomous melanoma diagnostic system will follow a structured
approach, broken down into several key stages. Each stage focuses on different aspects of the
system's design, development, and deployment to ensure the final solution is robust, accurate, and
efficient.
Figure 3: Project Methodology
The main stages are as follows:
4.1. Hardware Preparation
The first step involves selecting and preparing the hardware infrastructure necessary for training
and deploying convolutional neural networks (CNNs). High-performance computing resources,
including powerful GPUs or TPUs, will be utilized to handle the computationally intensive
processes of deep learning. The hardware will also need to support cloud-based services for
scalability and real-time diagnostic capabilities. Additionally, mobile devices such as smartphones
will be tested to ensure compatibility and smooth operation of the application.
4.2. Data Preparation
For this project, we utilized the publicly available HAM10000 dataset [20] published by Philipp
Tschandl in 2018 on the Harvard Dataverse platform. The dataset consists of 10,015 high-quality
dermatoscopic images covering a range of diagnostic categories, including actinic keratoses, basal
cell carcinoma, melanocytic nevi, melanoma, and vascular lesions. More than 50% of the lesions
were confirmed through histopathological examination, which is considered the gold standard for
skin cancer diagnosis, while the remaining cases were validated via follow-up examinations, expert
consensus, or in-vivo confocal microscopy.
Figure 4: Skin nevi from the base [20].
The preprocessing of the dataset involved several key steps to ensure the quality and
consistency of the images. First, color normalization was applied to account for variations in
lighting conditions and imaging devices, ensuring a uniform appearance across the dataset. Next,
noise reduction techniques were employed to remove any artifacts or unwanted disturbances,
thereby enhancing the overall image quality and enabling more accurate feature extraction.
Additionally, advanced fuzzy logic-based methods were used to further refine the segmentation of
the skin lesions, improving the accuracy of the subsequent feature extraction process.
To address the common issue of class imbalance in the dataset, we applied data
augmentation techniques. This included transformations such as rotations, translations, and flips,
which were used to artificially expand the size of the underrepresented classes. This approach
helps to prevent overfitting and enhances the generalizability of the model, enabling it to perform
well when encountering new, unseen data during the deployment phase.
Table 1
Division of the dataset according to classification
Lesion Type Train set Validation Set
Melanocytic Nevi (nv) 5954 751
Melanoma (mel) 1074 39
Benign Keratosis-like Lesions (bkl) 1024 75
Basal Cell Carcinoma (bcc) 484 30
Actinic Keratoses and Intraepithelial Carcinoma / 301 26
Bowen's Disease (akiec)
Vascular Lesions (vasc) 131 11
Dermatofibroma (df) 109 6
A comprehensive machine learning solution was developed to classify skin lesions, starting with
model creation and culminating in the deployment of a live web application. The primary objective
was to enable users to upload an image of a skin lesion and receive an instant diagnosis. The
system classifies skin lesions into seven categories, including melanocytic nevi, melanoma, basal
cell carcinoma, and more.
4.3. Model Building
The model employed for this task is a fine-tuned MobileNet Convolutional Neural Network (CNN),
chosen for its compact size and efficient performance, making it ideal for web and mobile
deployment. MobileNet's lightweight architecture enables real-time inference with lower
computational requirements while still maintaining strong performance in image classification
tasks.
Input Layer
The model accepts input images with a shape of (224, 224, 3), corresponding to RGB images
that have been resized to 224x224 pixels. This ensures that all input data is uniformly
processed by the network.
Convolutional Layers and Depthwise Separable Convolutions
Depthwise Separable Convolutions: The MobileNet architecture relies heavily on
depthwise separable convolutions, which separate spatial filtering from channel-wise
operations. This approach dramatically reduces the number of parameters and computation
required compared to traditional convolutions, while still capturing relevant image features
effectively.
Pointwise Convolution: Following each depthwise convolution, a pointwise convolution
(1x1) is applied, enabling the model to learn interactions between features across different
channels.
Batch Normalization and ReLU Activation: Each convolutional layer is followed by
batch normalization and ReLU activation to stabilize the training process and introduce
non-linearity, allowing the model to learn more complex patterns.
4.4. Model Training & Tuning
The model was trained using the Keras framework, employing a fine-tuned MobileNet architecture
for the classification of skin lesions. The training process was designed to maximize the model's
ability to generalize to new, unseen data while avoiding overfitting. Several techniques were
utilized to optimize the training process, including early stopping, learning rate reduction, and
model checkpointing.
To address the challenge of class imbalance in the HAM10000 dataset, where certain skin
lesion types were overrepresented, a combination of data augmentation techniques such as
rotations, flips, and zooms was applied to artificially expand the dataset. Additionally, class weights
were introduced to make the model more sensitive to melanoma (class weight = 3.0), helping to
mitigate bias toward more common lesions like benign nevi (class weight = 1.0).
Leveraging the pre-trained MobileNet model, we froze the weights of all layers except the
last 23 layers, allowing only these layers to be trainable. This approach enabled the model to retain
the general image features learned from the ImageNet dataset while fine-tuning the later layers to
specialize in melanoma detection based on the dermoscopic images from the HAM10000 dataset.
Hyperparameters such as the learning rate (initially set at 0.01), batch size, and the number
of epochs were optimized through trial and error. Callbacks like ModelCheckpoint and
ReduceLROnPlateau were employed to save the best model based on the top-3 accuracy metric, and
to reduce the learning rate if validation performance plateaued.
The model was compiled using the Adam optimizer with a learning rate of 0.01 and
categorical cross-entropy as the loss function. In addition to the standard categorical accuracy, we
introduced custom metrics like top-2 accuracy and top-3 accuracy to better capture the model's
ability to rank the correct diagnosis within the top few predictions.
During training, the model demonstrated consistent improvements, achieving strong
results after 30 epochs. By the end of training, the final model reached a validation accuracy of
89.5%, with a top-2 accuracy of 91.3% and a top-3 accuracy of 96.3% on the validation set. This
indicated that the model was able to rank the correct diagnosis within the top three predictions
96.3% of the time, making it highly effective in identifying the correct lesion type, even in
challenging cases.
After the best model was selected based on the top-3 accuracy metric, we evaluated its
performance on the validation data:
Final Validation Loss: 0.59
Categorical Accuracy: 80.8%
Top-2 Accuracy: 91.2%
Top-3 Accuracy: 96.2%
These metrics demonstrate that the model generalizes well and is highly capable of correctly
classifying both common and rare types of skin lesions, providing a reliable tool for melanoma
detection.
4.5. Model Deployment
To make the melanoma detection system accessible to a broad audience, a web-based application
was developed using a lightweight framework, leveraging modern web technologies to deliver a
seamless and user-friendly experience. The primary goal was to allow users, regardless of their
technical expertise, to upload an image of a skin lesion and receive an immediate, real-time
diagnosis. The system provides the predicted class (such as melanoma or benign lesion) along with
the associated probability, offering users an easy-to-understand result.
One of the unique aspects of this project is the conversion of the trained model, initially
developed in Keras, to TensorFlow.js, allowing the model to run directly within a web browser.
This approach ensures that the entire process, from image submission to the generation of
predictions, takes place locally on the user's device. As a result, no data is uploaded to external
servers, which preserves the user's privacy and makes the solution particularly well-suited for
medical applications, where data security and privacy are of paramount importance.
The conversion of the model from Keras to TensorFlow.js involved the following steps:
1. Recreating the Model in Native Keras: The trained Keras model was finalized and saved
in a format compatible with conversion.
2. Conversion to TensorFlow.js: The model was converted to TensorFlow.js using the
command-line conversion tool. This allowed the model to be served directly in the
browser without additional server infrastructure.
The model was then embedded within a basic HTML interface, which provides an intuitive and
simple user experience. The application allows users to:
1. Upload an Image: Users can select an image of a skin lesion from their device and upload
it directly through the browser.
2. Real-Time Processing: The model processes the image in real-time, leveraging the
TensorFlow.js framework to run the neural network in the browser.
3. Display of Results: The predicted lesion class, along with the top-3 probabilities, is
displayed on the page, giving users insights into the potential diagnosis.
The use of TensorFlow.js brings several key benefits:
Local Processing: Since the model runs entirely in the user's browser, the need for a
backend server is eliminated. This not only preserves privacy but also reduces
infrastructure costs and improves scalability.
Fast Inference: The conversion to TensorFlow.js ensures that model inference is fast,
enabling real-time feedback to users without noticeable delays.
Offline Capability: As the model is stored in the browser, it can potentially function even
in low-connectivity or offline environments, making it accessible to a wider audience.
4.6. Model Management
The development of this AI-powered skin lesion diagnosis system represents a significant
advancement in automated medical diagnostics. To maintain its effectiveness, continuous
monitoring and regular updates are essential. The model will be periodically monitored to ensure it
remains accurate as new data is collected. Retraining the model with fresh, labelled data will
prevent model drift and ensure high performance in detecting diverse skin lesions. Feedback from
dermatologists will be integrated to improve the system’s diagnostic accuracy, particularly in
handling ambiguous cases. This collaboration will ensure the model complements medical
expertise and maintains clinical relevance. Strict data management protocols will ensure
compliance with privacy regulations like GDPR and HIPAA. All patient data used for retraining
will be anonymized, ensuring secure, privacy-focused model updates. The web-based deployment
using TensorFlow.js allows for easy scaling, making the system accessible in underserved areas. As
the model evolves, it can be adapted for mobile use, further expanding its reach. By focusing on
these aspects, the system can continue to improve early detection of melanoma and provide lasting
benefits to global healthcare.
5. Results
This section outlines the performance of the AI-powered skin lesion classification model, along
with a real-world test case comparing the model’s prediction to the histopathological findings of an
actual patient.
The model's performance was evaluated using standard classification metrics: precision,
recall, and F1-score. These metrics provide a comprehensive view of how well the model
performs in classifying melanoma and other skin lesions.
Here is a summary of the model's performance:
Precision: Measures the proportion of correctly predicted instances among all instances
predicted as positive by the model. For example, for the "melanoma" class, the precision
indicates how likely the model’s melanoma prediction is to be correct.
Recall: Measures the proportion of correctly predicted positive instances out of all actual
positive instances. In other words, for the "melanoma" class, recall shows how well the
model can detect melanoma cases when they actually exist.
F1-Score: The harmonic mean of precision and recall, providing a single score that
balances both concerns, especially in cases where there is class imbalance.
Table 1
Classification Report
Precision (mel) Recall (mel) F1-score (mel)
0.27 0.48 0.34
This indicates that when the model predicts a lesion to be melanoma, it is correct only 27%
of the time. The model detects melanoma in 48% of the cases where it is present. The overall
performance for melanoma classification, balancing both precision and recall, is moderate with an
F1 score of 0.34.
These metrics highlight that while the model has room for improvement, it can detect
melanoma in some cases. Given the nature of skin lesion classification and the importance of early
detection, further tuning and adjustments may be required to improve the performance of the
model, particularly in real-world clinical applications.
Figure 5: Real skin nevus diagnosed for surgical excision
In addition to the evaluation metrics, the model was tested on a real case of a suspicious
skin lesion, where a physician recommended removal. The histopathological report confirmed as
compound melanocytic nevus, a benign condition (ICD-10: D22).
Model Prediction:
Melanocytic nevi: (97.8%)
Melanoma: (0.5%)
The model strongly predicted the lesion to be a benign melanocytic nevus, with a very low
probability of melanoma, which aligned with the final histopathological diagnosis. This real-world
validation supports the potential utility of the model in clinical scenarios, although more extensive
validation is needed to ensure broader applicability.
6. Discussion
The proposed method of diagnosing skin melanoma may also have negative effects and certain
limitations. There are risks where a mole scanning app will have downsides.
Firstly, due to the wide availability of diagnosis via mobile applications, many potential patients
may misinterpret the results and not further diagnostics of a specific nevus by a specialist.
Moreover, due to the wide availability of the tool and the limited diagnostic knowledge of
users, it is possible to practice self-treatment or create false security, which may ultimately prove
disastrous and lead to the progression of the neoplasm. Therefore, it is extremely important to
educate potential users before introducing this diagnostic tool, emphasizing the disadvantages of
the method and considering that it is not a fully professional diagnosis, which is only possible in
the case of contact with a specialist.
The limitation of the method, which depends on the type of user, is the possible lack of
effectiveness of the application due to the quality of the photo provided to the application by the
patient. An image of insufficient quality sent for analysis may make the diagnosis unreliable,
which may adversely affect further treatment.
6.1. Clinical Impact and Applications
The developed autonomous diagnostic system offers a rapid, non-invasive, and highly accurate
solution with broad potential applications across various healthcare environments. Its ability to
function autonomously without the need for specialised personnel makes it particularly valuable in
settings where access to expert dermatological care is limited, such as rural clinics or underserved
regions. This can significantly improve the accessibility of early detection and diagnosis, especially
for conditions like melanoma, which are critical for reducing morbidity and mortality. Moreover,
the system's compatibility with teledermatology platforms further enhances its utility by enabling
clinicians to provide timely assessments and treatment recommendations remotely, without the
need for in-person visits. This can streamline the diagnostic process, improve patient convenience,
and make quality dermatological care more accessible to a wider population, regardless of their
geographic location. Previous studies have shown that similar AI-based systems can aid in the
early detection of skin cancer, reducing the burden on healthcare systems and improving patient
outcomes. The integration of this technology into clinical practice has the potential to revolutionise
the field of dermatology, making high-quality care more readily available to underserved
communities.
6.2. Limitations and Future Directions
Despite its promising performance, the system faces several limitations that must be addressed in
future iterations. One significant challenge is the need to further refine the algorithm to account
for the heterogeneity in skin phototypes, ethnic backgrounds, and the wide range of morphological
presentations of skin lesions encountered in real-world clinical practice. Additionally, while the
model has demonstrated high diagnostic accuracy in controlled clinical settings, further large-scale
trials across diverse patient populations are necessary to assess its generalizability and robustness
Future research will focus on incorporating advanced imaging modalities, such as multispectral
imaging, to improve diagnostic accuracy and expand the system's capabilities to detect a broader
range of dermatological conditions These advancements, combined with continuous updates to the
model's training data, will be key to ensuring the system remains a reliable, accurate, and effective
tool in clinical dermatology.
The development of an autonomous diagnostic system using convolutional neural networks
represents a significant advancement in the field of automated melanoma detection by reducing
diagnostic times and improving the accuracy of early melanoma detection, this system has the
potential to revolutionize dermatological care, making high-quality skin cancer screening more
accessible and reliable for patients. Ongoing research and technological advancements will be
crucial in optimizing the system for broader clinical adoption and integration into routine clinical
practice.
6.3. Comparison with Existing Systems
While the proposed system demonstrates significant potential in improving the accuracy
and accessibility of melanoma detection, it is essential to compare its performance and approach
with existing AI-driven diagnostic systems. Recent studies [20, 21], have explored the capabilities
of convolutional neural networks (CNNs) in skin cancer classification. These studies employed
CNN-based architectures, similar to our system, for identifying melanoma and other skin lesions,
demonstrating accuracy levels comparable to those of trained dermatologists.
In Mahbod et al.'s work [21], a deep learning model was developed to classify skin lesions,
focusing on optimizing sensitivity and specificity for melanoma detection. Similarly, implemented
CNN architectures [22], examining their performance on large datasets to enhance classification
accuracy. However, these models were primarily evaluated in controlled environments with
limited real-world applicability due to factors such as image quality and lack of integration with
web-based platforms.
Our system advances these efforts by providing an accessible, web-based platform that
allows real-time image analysis without the need for backend servers. This approach not only
preserves user privacy but also facilitates a faster diagnostic process by performing all
computations locally on the user’s device. While systems [23, 24] focus on mobile and desktop
applications, our system is tailored for direct web browser use, making it highly accessible and
requiring minimal technical expertise from the end user.
Moreover, studies [24] highlight the ongoing challenge of dataset diversity. Existing
models often lack generalizability across different skin phototypes and ethnic backgrounds, which
can impact diagnostic accuracy in diverse populations. Our system addresses this limitation by
employing data augmentation techniques and transfer learning, though further dataset expansion
remains a priority for future improvement [24].
Despite its strengths, our system also shares limitations with these existing models, such as
the need for standardized image quality and the risk of overreliance on an AI-based preliminary
diagnosis. Thus, suggest, user education is crucial to avoid misinterpretation of results, a challenge
that all AI-based diagnostic systems face [23].
Our system aligns with the capabilities and challenges observed in other CNN-based
melanoma detection models, its web-based, real-time diagnostic feature distinguishes it from prior
approaches. Ongoing research is essential to further refine this technology and fully assess its
clinical utility and impact, especially through large-scale trials across diverse populations.
7. Conclusion
The development of our web-based melanoma detection system using convolutional neural
networks represents a promising advancement in dermatological diagnostics. By utilizing
dermoscopic images and employing sophisticated image processing, this system has demonstrated
a high potential to aid in the early detection of melanoma. With mobile accessibility and rapid
diagnostic capability, the system can expand access to initial screenings, especially in regions
lacking specialist dermatological resources.
Dermoscopy, while effective in improving melanoma detection accuracy, has limitations in
terms of equipment needs and subjectivity in assessment, particularly for non-expert users and
deeper lesions. Our system addresses some of these limitations by integrating automated
classification, which not only provides real-time results but also enables greater consistency in
evaluation. However, it remains complementary to traditional histopathology, which continues to
be crucial for confirming malignancy. Histopathological methods, while precise, are limited by cost,
access, and time constraints, underlining the utility of an adjunctive AI tool that can assist in
preliminary assessments.
Although the ABCDE criteria offer a valuable foundation for skin lesion evaluation, they
can be challenging for laypeople to apply accurately. Our system’s intuitive interface aims to
bridge this gap, allowing users to engage with melanoma detection independently while also
supporting clinicians in triaging cases that warrant further investigation. Future developments will
focus on expanding our dataset to improve model accuracy across diverse populations, refining
interpretability to enhance user trust, and integrating the system with telemedicine platforms to
foster broader clinical adoption.
Overall, our autonomous diagnostic system is a step toward democratizing skin cancer
screening, with the potential to improve outcomes by facilitating early detection and reducing
diagnostic wait times. Ongoing research, dataset expansion, and clinical trials will be essential for
optimizing and validating the system's real-world effectiveness. Through continued innovation,
this technology holds significant promise for enhancing the accessibility and accuracy of
melanoma screening on a global scale, ultimately improving patient care and outcomes.
8. Declaration on Generative AI
In the preparation of this work, the author employed tools such as ChatGPT to assist with
grammar and spelling verification, as well as paraphrasing and rephrasing. The content was
subsequently reviewed and refined by the author, who assumes full responsibility for the accuracy
and integrity of the final publication.
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