Santhi Durairaj, Parvathi Subramanian, Carmel Sobia Micheal Swamy
Keywords
Diabetic retinopathy • DCNN • Machine learning classifi ers • Kaggle APTOS
Abstract
Background Diabetic retinopathy (DR) is an eye disease developed due to long-term diabetes mellitus, which affects retinal damage. The treatment at the right time supports people in retaining vision, and the early detection of DR is the only solution to prevent blindness.
Objective The development of DR shows few symptoms in the early stage of progression; it is difficult to identify the disease to give treatment from the beginning. Manual diagnosis of DR on fundus images is time-consuming, costly, and liable to be misdiagnosed when compared to computer-aided diagnosis systems.
Methods In this work, we proposed a deep convolutional neural network for the recognition and classification of diabetic retinopathy lesions to identify the severity of the disease. The performance evaluation of the proposed model was tested with other machine learning classifiers such as K-nearest neighbor (KNN), Naïve Bayes (NB), logistic regression (LR), support vector machine (SVM), decision tree (DT), and random forest (RF).
Results Our proposed model achieves 98.5% accuracy for the recognition and classification of the severity level of DR stages such as no DR, mild DR, moderate DR, severe DR, and proliferative DR.
Conclusion The training and testing of our model are carried out on images from the Kaggle APTOS dataset, and this work can act as a base for the autonomous screening of DR.
