=Paper=
{{Paper
|id=Vol-3609/paper10
|storemode=property
|title=FedDDR: A Federated Improved DenseNet for Classification of Diabetic Retinopathy
|pdfUrl=https://ceur-ws.org/Vol-3609/paper10.pdf
|volume=Vol-3609
|authors=Akansha Singh,Krishna Kant Singh
|dblpUrl=https://dblp.org/rec/conf/iddm/SinghS23
}}
==FedDDR: A Federated Improved DenseNet for Classification of Diabetic Retinopathy==
https://ceur-ws.org/Vol-3609/paper10.pdf
FedDDR: A Federated Improved DenseNet for Classification of
Diabetic Retinopathy
Akansha Singha, Krishna Kant Singhb
a
SCSET, Bennett University, Greater Noida, India
b
Delhi Technical Campus, Greater Noida, India
Abstract
Damage to the retina and other eye blood vessels is the result of diabetes, a condition known
as diabetic retinopathy (DR). Affected individuals may have retinal clots, lesions, or
hemorrhaging. Exudates and lesions in the retina may cause visual loss in people with diabetic
retinopathy. Diabetic retinopathy identification is essential for effective patient care. This
research proposes a federated version of an enhanced DenseNet deep learning model for use
in the detection and classification of Diabetic Retinopathy in retinal fundus pictures. With the
dense blocks performing concatenation, the upgraded DenseNet model improves the feature
utilization efficiency. The model is trained using federated learning algorithm. Federated
learning enables distributed training of the model using remotely hosted datasets without the
need to gather data and, subsequently, damage it. This overcomes the limitations posed by the
data silos and makes full advantage of the existing medical data. The proposed model improves
the performance and ensures patient privacy by not gathering the data at a central dataset. The
federated average learning algorithm is used to train the model. The model uses Maximum
Probability Based Cross Entropy (MPCE) loss function. The proposed method's outcomes are
evaluated and contrasted with those of similar approaches. The results of this comparison
demonstrate that the suggested technique is superior to the others in terms of accuracy,
precision, and recall when applied to the categorization of retinal pictures.
Keywords 1
diabetic retinopathy, deep learning, Federated Learning, DenseNet
1. Introduction
Use Deep learning has emerged as a promising strategy for automated clinical diagnosis. The most
prevalent complications of diabetes are well-known to the general population. Many incidences of
avoidable blindness are caused by diabetic retinopathy, an eye condition that diabetics are prone to, but
which is not as well-recognized as other diabetes consequences. Diabetic retinopathy affects around
60% of people with type 2 diabetes and over 100% of those with type 1. The illness progresses through
four distinct phases, with the first two being the most manageable thanks to early detection and
subsequent preventive care. High blood sugar levels may damage blood vessels in the retina, leading to
diabetic retinopathy, an eye condition, as described by the American Academy of Ophthalmology.
There is a risk of a blockage and subsequent lack of blood flow if the afflicted blood vessels expand
and leak or seal up completely. Diabetic retinopathy may be very damaging to a person's eyes if left
untreated, thus finding it early is crucial. Possessing a reliable method for early detection of the illness
is crucial. The hazards associated with each stage of diabetic retinopathy are discussed here, as well as
the symptoms experienced at each stage and the medicinal interventions available to prevent further
progression of the disease. To diagnose and treat diabetic retinopathy in its earliest stages, it is essential
to take preventative measures, such as arranging yearly diabetic retinal exams.
IDDMβ2023: 6th International Conference on Informatics & Data-Driven Medicine, November 17 - 19, 2023, Bratislava, Slovakia
EMAIL: akanshasing@gmail.com (A. 1); krishnaiitr2011@gmail.com (A. 2)
ORCID: 0000-0002-5520-8066 (A. 1); 0000-0002-6510-6768 (A. 2);
Β© 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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Proceedings
These vital retinal examinations may discover hazardous problems before they cause significant
vision loss, giving the patient and their doctors the time to devise a treatment strategy. Patients and
doctors may use this action plan as a road map to better comprehend and address the far-reaching effects
of diabetes on a person's health. Photos of a healthy eye and a DR-affected eye are shown side-by-side
in Figure 1.
Figure 1: Eye structure and presence of DR [Image credit https://www.eyeops.com/]
Thus, to enable early detection of diabetic retinopathy computer aided methods are being widely
developed. In this paper, a federated deep learning model is proposed to detect DR using retinal
images. The use of Artificial Intelligence to help radiologists with computer-assisted patient
diagnosis has been generally successful, it is still difficult to create robust models with tiny datasets
at specific locations. The Early Treatment Diabetic Retinopathy Study (ETDRS) is only one of
many widely used diabetic retinopathy grading systems [1]. ETDRS uses a multi-tiered system to
categorize the finer, more nuanced aspects of DR. All seven fovea of the retinal fundus are
evaluated in this manner (FOV). The ETDRS is the standard, although the International Clinical
Diabetic Retinopathy (ICDR) [2] scale is employed instead because of its acceptance in both
clinical and CAD contexts [3]. This is because the ETDRS is hard to execute and has technological
limitations. There are less field-of-view (FOV) requirements for the ICDR scale, which specifies 5
severity levels and 4 levels for Diabetic Macular Edema (DME). It has been shown that
convolutional neural networks are effective in detecting and classifying DR [4]. Transfer learning
was used with CNN to significantly increase the performance of these networks [5]. The retinal
pictures in the medical image collection may be used to fine-tune the pretrained models using
transfer learning. These models were shown to be more precise than the standard CNN [6].
Ensemble approaches, which take the best features of several classifiers and combine them, have
been suggested by several academics. There is a greater information gain in the ensemble models
since they include the results of several individual models. Many different ensembling methods
exist for integrating complementary model data. For the DR issue, several ensemble classifiers have
been described and published in the literature [7-9].
The limited medical data availability is a major challenge for deep learning models. Collaboration
across several hospital is important to attain excellent algorithm performance when the number of
medical data samples is constrained. Due to technical, regulatory, or ethical issues, sharing patient
data frequently has restrictions. A neural network model with small sample size of biomedical
images could not be very generalizable. A multi hospital study could be used to overcome the
difficulties because it can greatly expand the sample size and sample variety.
Conventionally, the algorithm is trained on all patient data at a central location. But this strategy
has some drawbacks. First, sharing patient data that requires a lot of storage space (such as high-
resolution photographs) may be difficult. Second, legal, or ethical constraints prevent sharing part
or all patient data. Third, patient data is precious, thus institutions may not share it. Instead of
sharing data to central location, Federated learning based deep learning model may be more useful
and accurate.
With federated learning the repeated analysis of many databases and the exchange of
mathematical parameters (metadata) rather than real data that may disclose possible patient
identifiers is utilized [10]. Early applications of federated learning methods saw more uptake in
image classification and the improvement of wireless communication systems. Predictions on
healthcare outcomes such as mortality, ICU stay-time, hospitalization for cardiac events, dyspnea,
adverse medication responses, and more have recently been included into federated learning models
in the healthcare domain [11]. However, most applications of federated learning in healthcare
outcome prediction used relatively small datasets and partitioned the data theoretically (randomly)
to simulate the properties of actual data. In this research, we apply our framework to the Health Facts
data by using the information provided by the healthcare systems for each individual patient.
Most healthcare federated learning applications employed classification methods such logistic
regression, artificial neural network, multi-layer perceptron, support vector machines, and random
forest to construct federated predictive models. Existing methods for predicting complications from
diabetes, such as retinopathy (eye disease), neuropathy (peripheral nerve disorder), and nephropathy
(kidney disease), rely on centralized machine learning algorithms trained on small-size datasets from
the US population, which contain fewer than ideal numbers of cases of complications and less than
ideal patient information. In this research, we used a federated learning architecture to develop three
different machine learning models for binary classification of the occurrence of three different
diabetes-related complications: those affecting the eyes, the kidneys, and the peripheral nerves.
The existing deep learning and machine learning models have several limitations. These include
limited medical data availability, patient privacy issues and training overhead at a centralized
location. Therefore, in this paper a modified federated learning DenseNet model is proposed for the
classification if diabetic retinopathy. In this architecture, several sites may work together to train a
single global model. With federated learning, a global model is built by combining training results
from many locations without the need to share datasets. The confidentiality of the patients is
protected in this way. The global model's detection skills are further enhanced by the additional
supervision received from the findings of collaborating locations. When training AI models with
little data, this solves the problem of inadequate supervision. Thus, in this paper the above-
mentioned limitations are removed with the proposed Federated Learning Dense net model. The
initial model on the central server is initialized and the parameters are shared with the connected
devices. The results are simulated using tensorflow federated learning module. More than 5,000
retinal pictures from the third biggest dataset APTOS19 are segmented for use in virtual testing. The
DenseNet model used overcomes the vanishing gradient problem and strengthens the feature
propagation as features are concatenated at each stage.
This paper is organized into five sections in the first section the introduction to the problem and
literature review is discussed. The second section discusses the proposed methodology followed by
results and discussion section. The last section gives the overall conclusion of the work presented in
this paper.
2. Proposed Method
The traditional machine learning models that are trained centrally on one device pose some serious
challenges when used for healthcare applications. The limited availability of data due to multiple
constraints of data privacy and sharing is a major issue. Therefore, in this paper a DenseNet model
with federated learning approach is presented. Data privacy, data security, data access rights, and access
to heterogeneous data may all be addressed by using federated learning, which allows several hospitals
to construct a shared, robust machine learning model without sharing data. Therefore, federated
learning models may collect data from several sources (e.g., hospitals, electronic health record
databases) to give more diverse data. In this section the steps involved in the proposed methodology
are discussed in detail. In figure 2 the proposed methodology is shown. The central model is trained
using N connected devices. Each device uses its own dataset for training and transmits the updates in
the model weights.
Figure 2: Proposed Methodology
The general training mechanism is shown in figure 3.
Figure 3: Training at central and connected devices
1. Initial Model Configuration : The DenseNet model [12] is initialized at the central server device.
The training of the central model is done using the APTOS2019 dataset. The initial parameters of the
model are then transmitted to each of the connected devices.
2. Training at connected devices: A copy of the model is available at each of the connected and it uses
the parameters broadcasted by the server. The following steps are followed at each connected device.
3. Input Retinal Images: The retinal pictures are fundus images captured under a variety of lighting
and camera angles. A doctor assigns a score from zero to four for five categories to each picture,
reflecting the severity of diabetic retinopathy. The model is tested and trained using these pictures.
4. Pre-processing of images: The photos are shot in a variety of environments with varying levels of
illumination. Before they may be utilized for model training, these photos need preprocessing. Due to
the lack of contrast in retinal pictures, CLAHE is used to equalize the histograms [13]. The CLAHE
histogram equalization is computed as follows:
$!"# %$!$%
π !"# = $&
(1)
The proposed deep learning network receives its input data from the pre-processed images acquired in this
stage.
5. Image Resizing: As the images at different connected devices are of different size and thus, they need
to be resized before feeding to the deep network. Thus, all images are resized to 224 Γ 224 pixels.
6. Image Standardization: Image standardization is a data transformation technique. Standardization
rescales the image features so that the mean is 0 and standard deviation is 1. This improves the
optimization and consequently the accuracy of the model.
$% )'
π &' = (2)
*'
where ππ is the mean and ππ is the standard deviation.
7. DenseNet Model: A DenseNet model is used for the classification of the retinal images for
identification of diabetic retinopathy. Using Dense Blocks, in which we directly link all layers (with
matching feature-map sizes) with each other, a DenseNet is a sort of convolutional neural network that
makes use of dense connections between layers. To maintain the feed-forward structure, each layer
pulls in data from the layers below it and sends its own feature maps to the layers above it. DenseNet
s have outperformed traditional CNNs and ResNets on a wide variety of benchmark datasets, and their
smaller model size is a consequence of both factors.The architecture of the DenseNet 121 used is as
follows:
Figure 4: Model Architecture for the proposed method
In each layer the feature maps of the preceding layers are concatenated as input. Due to concatenation
the features are not repeated, and redundant features are removed. Each lth layer receives the feature
maps of the previous layers.
π₯+ = π»+ ([π₯, , π₯- , β¦ . , π₯+%- ]) (3)
where [ ] denotes concatenation operation and π»π is a composite function. It comprises of batch
normalization (BN), a rectified linear unit (ReLU) and a convolution (Conv).
DenseBlocks are the building blocks of DenseNet; the size of the feature maps stay the same inside a
block, but the number of filters varies. By removing one layer of transition between each block, we
may cut the total number of channels in half.
The amount of information to be added in each layer is controlled by the growth rate (k) of DenseNet.
Thus, in lth layer the amount of information added can be computed as:
π+ = π. + π β (π β 1) (4)
where π0 is the number of channels in the input layer.
8. Maximum probability based cross entropy loss: For the sake of fine-tuning the model-learning
process an MPCE loss function is implemented [20]. Because of this, the convergence is accelerated,
and the back propagation error is minimised. MPCE may be expressed mathematically as in eq (5).
π ' (π) = β β1 0 1
/2- π¦/ πππ(π¦/ ) = β β/2-(π¦134 β π¦5 )π¦
9πππ(π¦
6 /) (5)
where, π¦πππ₯ where the real class, π’π‘β among m classes, is the largest. If π¦ ! is a vector of real classes,
then the uth coordinate is 1, and π¦ is the vector of uth coordinates. The i-th coordinate of the vector π¦β²
!
is denoted by π¦β²π .
9. Adam Optimization: In order to maximise efficiency, the adam optimizer combines the benefits of
both the Momentum and Root Mean Square propagation methods [14]. When the gradient hits its
global minimum, ADAM slows the pace of descent such that there is little oscillation.
78 78 9
π' = π½- π'%- + (1 β π½- ) <7# = π£' = π½9 π£'%- + (1 β π½9 ) <7# = (6)
( (
10. Federated averaging Learning: Federated averaging algorithm uses an averaging method to combine
the updates at the central server [15]. A network of N devices available at N different hospitals, indexed
π β {1,2, β¦ , π}. Each device or hospital has its own dataset consisting of retinal images denoted by
π·π . Each π·π comprises of an input vector π₯π and an outcome variable π¦π . The model will be trained
using this network of devices. Thus,
π# = π₯: β π¦
@: (7)
π₯π is the input feature vector and π¦"π is the predicted output using vector w and loss function.
The local loss at each device can be computed as,
πΉ; (π€) = (1β|π·; |) β:<=& π: (8)
The assumption in this problem is ,
|π·π | = $π·π $ βπ, π (9)
Thus, the optimization is the average over the πΉπ (π€). The objective is to find w that minimizes π(π€)
over the data π· = π. π·.
min -
π(π€), π€βπππ π(π€) β > β>
;2- πΉ; (π€) (10)
π€
1
In case |π·π | β $π·π $ then π can be replaced with ππ = |π·π |β|π·|
The complete algorithm for the training process is as follows:
Algorithm: FedDDR learning
Input: K [Number of Hospitals/Devices],T[epochs],ππ [Initial weight vector],πΆ
[learning rate of client], πΈ[Learning rate of server]
Start
Server broadcasts ππ to K devices.
For t-0,β¦T-1
For each device π = π, β¦ , π² computes ππ+π π
Each devices sends the ππ+ππ back to the server
Server averages and updates the w as
π π+π
ππ+π = ππ + π² βπ² π=π ππ
Output the final model parameters ππππππππ
11. Termination Condition: The training is terminated when the number of iterations is complete, or the
model has converged to the optimal solution.
12. Grad Cam Visualization: The Gradient based Class Activation Map (Grad-CAM) is an example of
a class-discriminative localization map that draws attention to important parts of an image by
calculating the gradient of the class score yc for class c relative to the activations Ak of a convolutional
layer's feature map βyc /βAk. ack is the result of a global-average-pooled backflow of these gradients,
which is used to derive the significance of neuronal weights [16].
N
- OPOQ
β?; = @ β/ βA (11)
Grad-CAM is basically a weighted combination of forward activation maps followed by ReLU
operation as follows:
πΏππΊπππβπΆπ΄π = π
ππΏπ Fβπ βππ π΄π H (12)
With the help of the Grad-CAM visualisation heatmap, we can see how the three categories our model predicts
for test photos are distributed among a set of representative examples. Grad-heatmap CAM's depiction draws
attention to the key pixel clusters used by the model's last convolution layer to make class distinctions. Here, we
see how the GRAD-CAM visualisation system distinguishes between standard and DR photos by highlighting
them in a variety of ways. Class activation maps for both normal and DR photos show that the centre of the image
is emphasised more strongly in the former instance, while the top part of the image is illuminated more densely
in the latter. Important visual features utilised by the model to make the concept prediction are highlighted in the
class activation map. Figure 5 displays several example gradcam representations of retinal images.
Figure 5: GradCam visualizations of Retinal images
3. Experiments
The proposed method is tested and its performance is measured by implementing it in Python. The dataset is
large and thus GPU acceleration is used for the simulation purpose. Keras module in Python is used for developing
the deep network and Tensorflow Federated Learning for training. The initial base model is trained using the
following datasets. Thereafter, each client uses its own dataset. For simulation purpose the dataset was divided
into different clients. The dataset used is from APTOS 2019, a dataset on diabetic retinopathy
(https://www.kaggle.com/c/aptos2019-blindness-detection). Aravind Eye Hospital collects the data in rural India
so that it may be used to create AI for DR detection. All the images fall into one of five categories: No DR, Mild,
Moderate, severe and Proliferative DR. The severity, location, and frequency are taken into account when
assigning a grade from 0 to 4.
The dataset comprises of a total of 3662 images. For experiments the images are split into training and testing
in the ratio of 80:20. Thus, 2930 images are used for training and 732 images are used for testing. The sample
images from the database are shown in figure 6.
(a) (b) (c) (d) (e)
Figure 6: Retinal images for (a) Normal (b) Mild DR (c) Moderate DR (d) Severe DR (e) Proliferative DR
We use these metrics to measure how well the suggested technique performs. Specifically, these indicators are
employed:
The number of incorrect predictions is the standard measure of accuracy, or precision. You can figure this out
by
LM
ππππππ πππ = LMNOM (13)
For each given model, recall indicates how many true positives it generates. In eq.(14), we see the formula for
calculating the recall:
LM
π
πππππ = LMNO> (14)
Total accuracy is calculated using the same method.
LMNL>
ππ£πππππ π΄πππ’ππππ¦ = LMNL>NOMN O> (15)
where TP, FP and FN represents the true positive, false positive and false negative, respectively.
F1-score can be computed using eq.(16)
ππππππ πππΓππππππ
πΉ1 = 2 Γ ππππππ πππ+ππππππ (16)
4. Results and Discussion
In this section the results obtained from the proposed method and a comparative analysis is presented. The
results are obtained by performing simulations and the data was split amongst different clients to observe the
results. Federated data set, i.e., a collection of data from multiple users is required for demonstrating the proposed
method. Thus, to facilitate experimentation, the data set was split amongst five users. Due to individual
differences in data consumption behaviors [21, 22], federated data is often not identically distributed among users.
Due to data scarcity on the device, some customers may have fewer training instances than others, while other
clients may have more than enough. Because this is a simulated environment, we have access to all the data
required to do such a comprehensive examination of a client's data. In a fully operational federated setting, it is
impossible to see the data of a single client.
Table 1:
Data Splitting for five devices
Dataset Splitting for 5 devices
Grade D1 D2 D3 D4 D5 Total Images
0 -No DR 144 360 285 304 351 1444
1 β Mild 40 56 62 66 72 296
2-Moderate 125 184 116 196 178 799
3- Severe 20 45 44 34 34 155
4- Proliferative 110 54 57 42 32 236
An extremely large number of user devices may be involved in a typical federated training scenario, yet only a
subset of these devices may be accessible for training at any one moment. For instance, when the client devices
are mobile phones, they can only take part in the training when they are fully charged, not using the network, and
not being charged. Since this is a simulation, all the information we need is already on-hand. So, when we
conducted simulations, we would usually choose a new group of customers to train with each time.
The parameters used for simulating the federated learning environment is as follows:
Table 2:
Parameters Used for Simulation
Number of Clients 5
Client Optimizer (Local model Updates) Adam Optimization
Server Optimizer (averaged update to the global model Adam Optimization
at the server)
Learning Rate (Client) 0.001
Learning Rate (Server) 0.001
epochs (Server) 60
All DR pictures in the dataset fall into one of five categories, labelled with the digits 0-4.
0 β No DR (NDR) 2: Moderate 4: Proliferative DR (PDR)
1: Mild 3: Severe
732 test photos from a range of grading levels were used in the analysis. Table 4 displays the distribution of
photos by grade level.
Table 3:
Testing image distribution
The five-class confusion matrix is shown in table 6.
Grade Testing images
0 -No DR(NDR) 353
1 β Mild 87
2-Moderate 205
3- Severe 40
4- Proliferative (PDR) 48
Total 733
Table 4
Confusion Matrix for five classes
Predicted
NDR Mild Moderate Severe PDR
NDR 344 2 3 1 3
Actual
Mild 3 76 4 2 2
Moderate 3 2 195 3 2
Severe 2 1 1 35 1
PDR 1 2 2 3 41
Based on the confusion matrix the following metrics are computed
Table 5
Metrics computed from Confusion matrix
Class Precision (%) Accuracy (%) Recall F1 Score
(%)
NDR 97 97.54 97 97
Mild 87 97.68 93 90
Moderate 95 97.27 95 95
Severe 88 98.09 80 83
PDR 85 97.95 84 85
Overall Accuracy 94.27%
The resulting findings are compared to those of other cutting-edge approaches. Table 8 displays the outcomes
of the various approaches used across the five categories.
Table 6
Results Comparison five classes
Method Precision (%) Recall (%) Accuracy (%)
DRISTI (VGG16 + Capsule) [17] 91 88 82.06
EfficientNet-B3 [18] 59 66 84.86
Resnet50 + Capsule [19] 59 69 76.80
FedDDR (Proposed Method) 89 90 94.27
5. Conclusion and Future work
In this paper, a federated deep learning model for detection of diabetic retinopathy from retinal images. The
retinal fundus images are classified into five classes. A modified DenseNet model with federated learning
approach is proposed in this work. The federated learning makes the training process distributed and hence
improving the overall performance of the classifier. The patient privacy is also intact as their data remains on
their device. Also, the limitation of limited medical data for training is overcome as the data is used from multiple
devices. The simulations are done using tensorflow federated learning module. The results show that the proposed
method achieves 94.275 overall accuracy and class wise accuracy is also high. The comparison with other state
of the art methods reveal that the proposed method outperforms the existing state of the art methods.
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