Exploring Deep Neural Networks for Accurate Diabetic Retinopathy Prediction

Diabetic Retinopathy (DR) emerges as a serious complication associated with diabetes mellitus, causing damage to retinal blood vessels, and resulting in vision loss. The critical need for early detection and effective management to curb the progression towards vision impairment and potential blindness underscores the significance of this issue. To address these challenges, have propose a novel deep learning system utilizing a Densely connected Convolutional Network (DenseNet-201) for the efficient detection of DR. This model is specifically trained to classify fundus images into six distinct categories: no DR, mild, moderate, and severe, proliferative DR, and hard exudates. The datasets utilized in this study include the publicly available Diabetic Retinopathy Detection 2015 and Aptos 2019 blindness detection datasets from Kaggle. The proposed system comprises multiple stages, including Data Collection, Preprocessing, Augmentation, and Modeling. During the preprocessing phase, normalization techniques are to eliminate biases or distortions present in the fundus images. Furthermore, the augmentation of data is employed to enhance the diversity and quantity of the training dataset. In the modeling stage, have designed and implemented the deep learning model, with the robust and high-performing DenseNet-201 architecture selected as the model's backbone. The model exhibits a significant accuracy of 87.7% in its classification task. Additionally, have introduced a Deep Neural Network (DNN) model, achieving an impressive accuracy rate of 95.8%. These results underscore the effectiveness of our proposed system in accurately and efficiently detecting DR. The primary objective of this research is to develop a reliable and automated system for Diabetic Retinopathy detection, with the overarching goal of improving the quality of life for individuals affected by diabetes.