Diabetic Retinopathy Using Deep Learning and CNN

Diabetic retinopathy (DR) is an eye disease triggered by diabetes, which can lead to permanent vision loss or blindness. To prevent diabetic patients from becoming blind, early diagnosis and accurate detection of DR are vital. Deep learning models and convolutional neural networks (CNNs) are widely used in diabetic retinopathy (DR) detection by classifying blood vessel pixels from the surrounding pixels. In this paper, an improved activation function was proposed for diagnosing DR from fundus images that automatically reduces loss and processing time. The model is then fine-tuned, such that the low-level layers learn the local structures of the lesion and normal regions. As the fully connected (FC) layers encode high-level features, which are global in nature and domain-specific, we replace them with a new FC layer based on the principal component analysis PCA and use it in an unsupervised manner to extract discriminate features from the fundus images. This step reduces the model complexity, significantly avoiding the overfitting problem. This step also lets the model adopt the fundus image structures, making it suitable for DR feature detection. Finally, we add a gradient boosting-based classification layer. The evaluation of the proposed system using 10-fold cross-validation on two challenging datasets (i.e., Eye PACS and Messidor) indicates that it outperforms state-of-the-art methods. It will be useful for the initial screening of DR patients and will help graders in deciding quickly as regards patient referral to an ophthalmologist for further diagnosis and treatment.