Deep Learning Regression-Based Retinal Layer Segmentation Process for Early Diagnosis of Retinal Anamolies and Secure Data Transmission through ThingSpeak

Abstract

Diabetic retinopathy (DR) is a progressive type of problem that affects diabetic people. In general, this condition is asymptomatic in its early stages. When the condition progresses, it can cause hazy and unclear vision of objects. As a result, it is necessary to develop a framework for early diagnosis in order to prevent visual morbidity. The suggested method entails acquiring fundus and OCT images of the retina. To acquire the lesions, techniques such as preprocessing, sophisticated Chan–Vese segmentation, and object clustering are used. Furthermore, regression-based neural network (RNN) categorization is used to achieve expected results that help foretell retinal diseases. The methodology is implemented using the MATLAB technical computing language, together with the necessary toolboxes and blocksets. The proposed system requires two steps. In the first stage, the detection of diabetic retinopathy via the proposed deep learning technique is carried out. The data collected from the MATLAB are transmitted to the approved PC via the IoT module known as ThingSpeak in the second stage. To validate the robustness of the proposed approach, comparisons with regard to plots of confusion matrices, mean square error (MSE) plots, and receiver operating characteristic (ROC) plots are performed.