Convolutional Neural Network-Based Classification of Multiple Retinal Diseases Using Fundus Images

Use of deep learning algorithms for the investigation and analysis of medical images has emerged as a powerful technique. The increase in retinal dis-eases is alarming as it may lead to permanent blindness if left untreated. Automa-tion of the diagnosis process of retinal diseases not only assists ophthalmologists in correct decision-making but saves time also. Several researchers have worked on automated retinal disease classification but restricted either to hand-crafted fea-ture selection or binary classification. This paper presents a deep learning-based approach for the automated classification of multiple retinal diseases using fundus images. For this research, the data has been collected and combined from three distinct sources. The images are preprocessed for enhancing the details. Six layers of the convolutional neural network (CNN) are used for the automated feature extraction and classification of 20 retinal diseases. It is observed that the results are reliant on the number of classes. For binary classification (healthy vs. unhealthy), up to 100% accuracy has been achieved. When 16 classes are used (treating stages of a disease as a single class), 93.3% accuracy, 92% sensitivity and 93% specificity have been obtained respectively. For 20 classes (treating stages of the disease as separate classes), the accuracy, sensitivity and specificity have dropped to 92.4%, 92% and 92% respectively.