Diabetic Retinopathy Using Deep Learning and CNN

 Diabetic Retinopathy Using Deep Learning and CNN

1Y. SAI ARUN TEJA, 2V. MAHENDRA, 3V. PRAVEEN

1 Y. SAI ARUN TEJA of CSE Departement
2 V.MAHENDRA of CSE Departement

3 V.PRAVEEN of CSE Departement

Abstract

Diabetic retinopathy (DR) is an eye disease triggered due to diabetes, which may lead to 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 largely used in DR detection through the classification of blood vessel pixels from the remaining 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 layer (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 a 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.

Keywords: Diabetic retinopathy, fundus images, CNNs, activation function.