The skin of the face is a complex organ, the condition of which can vary due to various factors such as genetics, lifestyle, environmental conditions and age. An accurate assessment of the condition of the skin is critical for the correct selection of care and treatment methods, which stimulates the development of technologies for its analysis. Recent advances in artificial intelligence (AI) provide new opportunities for automated skin analysis, which improves the accuracy of diagnosis and the efficiency of procedures. This work focuses on the review and analysis of the application of facial skin analysis systems integrated with artificial intelligence algorithms. It is important to understand the working principles of such systems and their potential in practical application both in cosmetology and in other medical fields. The purpose of this work is to study the technical aspects of creating such skin analyzers, in particular their software part, and to evaluate their practical effectiveness based on modern machine learning algorithms.
Skin health is a critical aspect of overall well-being, and early detection of skin conditions plays a vital role in effective treatment and prevention. Facial skin, in particular, is highly susceptible to various dermatological issues, such as acne, hyperpigmentation, dryness, and signs of aging. Traditional skin diagnostic methods often require clinical expertise, specialized equipment, and time-consuming procedures. In recent years, advancements in artificial intelligence (AI) have offered new opportunities to enhance skin diagnostic processes, providing efficient and accurate solutions. Artificial intelligence, in particular machine learning, is opening new horizons in skin diagnostics, allowing the creation of systems capable of analyzing skin images and making recommendations for care and treatment based on identified problems.
At this stage of technology development, there are already devices that allow you to assess the condition of the skin, both companies and science offer new approaches to facial skin diagnostics. This paper proposes an AI-based method for face skin diagnostics, utilizing data obtained from a smart skin analyser. The smart skin analyser collects comprehensive facial skin data, including moisture levels, pigmentation patterns, pore size, and other relevant features. By integrating this data with AI algorithms, the proposed method aims to automate and optimize the diagnostic process, delivering reliable and consistent results.
Our approach leverages neural network application and image processing algorithms to analyse the facial skin's characteristics. The proposed method identifies various skin conditions and provides personalized recommendations based on the analysed data and offers a non-invasive, efficient, and accessible solution for users seeking professional-level skin analysis and care in realtime.
In the course of the study, an analysis of the latest scientific publications in the field of skin diagnostic was carried out.
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It is impossible not to notice that along with scientific developments, many new devices are appearing on the market that allow to measure skin parameters and even predict the result after surgical correction in plastic surgery.
For example, the VECTRA H2 imaging system [
3D LifeViz® Mini [
LifeViz® technology allows you to quantitatively change the volume and determine the small
details of the skin surface with extreme accuracy. An example of an image created by 3D LifeViz®
is presented in Figure 1.
Taking into account the relevance of the problem of application modern information technologies
in medical area, namely, analysis of the results taken from the Smart skin analyzer [
Therefore, the purpose of the research is: 1) to consider convolutional neural network architectures for medical image analysis; 2) to evaluate the effectiveness of models for the task of detecting and classifying skin defects; 3) to consider possible ways of improving the performance of models by changing the input parameters.
The data for the experiment were obtained using the skin analyzer AISIA [
As machine learning technologies demonstrate their effectiveness in the analysis of medical
images [
The main idea of ResNet is to learn the residual mapping [
Thus, H(x) from formula (1) can be represented as:
F(x) = H(x) − x, H(x) = F(x) + x, (1) (2)
A typical residual block consists of two or more convolutional layers with batch normalization (BN) and ReLU activation:
y = F(x; {Wi}) + x, where: Wi – weights of the convolutional layers; y – output of the block.
Key Properties of ResNet are Identity Mapping and Training Deep Networks. The skip connections enable identity mapping, facilitating gradient flow and addressing the vanishing gradient problem. ResNets can have hundreds or thousands of layers while remaining easier to train compared to traditional deep networks. 3.2.
In DenseNet, each layer receives inputs from all preceding layers. The output of layer can be computed as: =
( 0, 1, . . . , −1), where: Hl – operations performed by the layer; xi – feature maps from all preceding layers.
Instead of adding the inputs as in ResNet, DenseNet concatenates feature maps:
= −1, −2, . . . , 0, (5)
Key Properties of DenseNet are Feature Reuse and Gradient Flow [
EfficientNet uses a compound scaling method, which balances the scaling of depth d, width w, and resolution r. The scaling can be defined as: = , = , = , where: k – constant; α, β, γ – scaling coefficients.
EfficientNet starts from a baseline model and scales it. For instance, the total number of parameters P in the model can be expressed as:
= ∙ 2 ∙ 2, (7) where c – constant that defines the efficiency of the architecture.
Key Properties of EfficientNet are Optimized Architecture and Efficiency. The architecture is
optimized through neural architecture search, allowing for a balance between model size and
performance. EfficientNet achieves state-of-the-art accuracy with fewer parameters compared to
previous models [
MobileNet introduces depthwise separable convolutions, which factor the standard convolution
into two separate layers: Depthwise Convolution and Pointwise Convolution [
Depthwise Convolution can be represented as a single filter, which is applied to each input channel.
= ⊙ , (8)
Pointwise Convolution can be represented as a 1×1 convolution that combines the output from the depthwise layer.
= ( ) = ∙ , (9)
Key Properties of MobileNet are Efficiency and Width Multiplier. The reduction in the number of parameters and computations compared to standard convolutions makes MobileNet highly (3) (4) (6) suitable for mobile and edge devices. MobileNet allows for a width multiplier α to reduce the number of channels in each layer, further optimizing the model size. 3.5.
The Skin Disease Classification Dataset from Kaggle [
For detecting skin defects such as acne, redness, etc., the choice of CNN architecture will depend on factors such as dataset size, problem complexity, and available computing resources. The most commonly used CNN architectures include: – Residual Networks (ResNet); – Dense Convolutional Network (DenseNet); – EfficientNet; – MobileNet.
Table 1 shows a comparison of the capabilities of the presented architectures.
Using ResNet enables deep learning, thanks to the use of residual connections, which allows you to train deeper networks without facing the gradient vanishing problem. This can help the model capture more complex patterns in skin texture and blemishes. The model shows high performance in the analysis of medical images.
Pre-trained ResNet models (such as ResNet-50 or ResNet-101) can be fine-tuned on datasets to improve performance, especially when using limited datasets.
This model is recommended for complex tasks where high accuracy is required.
DenseNet connects each layer to all other layers, which promotes feature reuse and results in stronger gradients. This is useful when detecting subtle skin imperfections such as acne or discoloration.
Compared with ResNet, DenseNet achieves high accuracy using fewer parameters, which can reduce training time while maintaining high performance.
DenseNet is widely used in medical image classification tasks, so the model can be effective for dermatological image analysis.
The model is recommended for medium and large data sets with a focus on achieving high accuracy, especially in conditions of limited computing resources.
EfficientNet has high performance while using less computation. The model systematically scales width, depth, and resolution, making it extremely efficient in terms of both accuracy and computational resources.
The EfficientNet pre-trained models are very effective, when fine-tuned, for medical imaging tasks, including skin defect detection. EfficientNet has been proven to outperform models such as ResNet and DenseNet in various medical image classification tasks and, at the same time, is more resource efficient.
The use of the model is recommended for projects that require a balance between high accuracy and efficiency, especially when working with large-scale images or in cases of limited computing power.
MobileNet is a lightweight and fast model optimized for efficiency and ideal for real-time detection of skin defects on mobile or embedded devices.
The model has low computational requirements because it uses depth-separated convolutions, greatly reducing the number of parameters. Thus, the model is ideal for applications where computing resources are limited.
Based on the above, it can be concluded that the model is suitable for deployment on mobile platforms and is an excellent solution for applications that detect skin defects using a smartphone camera.
Testing of the proposed models revealed that the ResNet and EfficientNet models demonstrate low results on the test sample, while DenseNet and MobileNet perform the task of recognizing skin defects with high accuracy (Figure 3).
gradients.
Deeper networks improve performance.
Robust and widely used.
Reduces number of parameters.
Efficient feature reuse and learning. Good for small datasets.
less computation. Flexible architecture for different constraints.
Scalable.
Highly efficient on resourceconstrained devices.
Lightweight and fast.
Suitable for mobile applications.
size for deeper versions.
Computationally intensive on larger ResNet variants.
computational cost in memory due to dense connections. More prone to overfitting on small data.
Heavier versions still require substantial computational power. – Lower accuracy compared to larger networks. – Limited model capacity for complex tasks.
Applications Image classification, object detection, face recognition
The loss rate quantifies the error the model makes on the test set. Usually reflects how well the predicted probabilities agree with the actual values. This indicator was the lowest for MobileNet, and DenseNet was somewhat inferior (Figure 4). The loss indicators for ResNet and EfficientNet turned out to be too high, so it was concluded that it is not appropriate to use these models for the task of classifying skin defects.
Although the models show high accuracy and low loss on the training data set, on the validation set the accuracy is slightly lower and the loss is higher, indicating overfitting of the model. Thus, we propose to change the approach to the formation of the dataset, by including more parameters, in particular, adding images made using the ultraviolet spectrum, as well as converting images into heat maps. This will allow for a more qualitative assessment of the image and contribute to a more accurate determination of the nature of the defect. In addition, it is advisable to use sensors to determine skin moisture. The main parameters of the proposed dataset are listed in Table 2.
Skin health is an essential component of overall well-being, and the early identification of skin conditions is crucial for effective treatment and prevention. The facial skin, in particular, is especially prone to various dermatological concerns, including acne, hyperpigmentation, dryness, and signs of aging.
In the course of this study the main models of convolutional neural networks are considered, the peculiarities of their application in the tasks of medical image analysis, in particular for the detection of facial skin defects, are analyzed. The advantages and disadvantages of the most widely used architectures, as well as the features of their application, are described. The models were tested on a dataset including medical images. The effectiveness of the models was evaluated according to the accuracy and loss metrics. On the basis of the conducted research, the structure of the dataset is proposed, based on a larger number of parameters, which will significantly improve the accuracy of the models by selecting the most relevant features and analyzing them.
For further research, there are plans to systematically identify and extract the key features that are critical for effectively classifying skin defects through the application of Convolutional Neural Networks (CNNs). This involves conducting an in-depth analysis of the various characteristics present in the dataset, such as texture, color variations, and the specific patterns associated with each skin condition. By leveraging techniques like feature extraction and selection, we aim to isolate the most informative attributes that contribute to the accurate identification of skin defects. This process will not only enhance the performance of the CNN models but also improve their interpretability, allowing for better understanding and insight into how these models make classification decisions. Furthermore, this feature extraction phase will enable us to refine the model architectures and optimize hyperparameters, leading to more robust and generalizable outcomes. Ultimately, this research will contribute to the development of more precise and reliable diagnostic tools in dermatology, potentially transforming the way skin conditions are assessed and treated in clinical practice.
The authors would like to thank Healthy Face Clinic and Dr. Vladyslav Karabaiev for providing the equipment for data gathering that made the experiments and this work possible.
It was used ChatGPT and Microsoft Copilot to rephrase sentences to improve style. The text was carefully checked by the authors after using these software. The authors take full responsibility for the publication. 9. References