A novel few-shot classification framework for diabetic retinopathy detection and grading. (15th August 2022) - Murugappan, M.; Prakash, N.B.; Jeya, R.; Mohanarathinam, A.; Hemalakshmi, G.R.; Mahmud, Mufti
Highlights: We proposed a fully automated Computer-Aided Diagnosis system for Diabetic Retinopathy (DR). Two specific problems have been addressed, namely, DR detection and DR grading for severity assessment. Novel deep learning framework called DRNet has been developed for DR detection and DR grading. Maximum mean accuracy, sensitivity and specificity of 99.72%, 99.86%, and 99.62% achieved for DR detection. Achieved a maximum mean accuracy of 99.18%, sensitivity of 97.41%, and specificity of 99.56% in DR grading. Abstract: Diabetes Retinopathy (DR) is a major microvascular complication of diabetes. Computer-Aided Diagnosis (CAD) tools for DR management are primarily developed using Artificial Intelligence (AI) methods, such as machine and deep learning algorithms. DR diagnostic tools have been developed in recent years using deep learning models. Thus, these models require large amounts of data for training. Consequently, these huge amounts of data are not balanced due to fewer cases in the dataset. To solve the problems associated with training models with small datasets, such as overfitting and poor approximation, this paper proposes a paradigm called Few-Shot Learning (FSL) which uses a relatively small amount of training data to train the models effectively. This paper proposes a novel prototype network, a type of FSL classification network capable of grading and detecting DR based on attention . The DRNet framework uses episodic learning to train its model on few-shot classification tasks. We developed a DRNet based on the APTOS2019 dataset for diabetic detection and grading. In the proposed network, aggregated transformations and gradient activations of classes are leveraged to design the attention mechanism to capture image representations. As a result, the system achieves 99.73 % accuracy, 99.82 % sensitivity, 99.63 % specificity in DR detection, 98.18 % accuracy, 97.41% sensitivity, and 99.55% specificity in DR grading. An analysis of objective performance metrics and model interpretation shows that the proposed model can detect DR more efficiently and grade the severity more accurately when using unseen fundus images than existing state-of-the-art methods. Therefore, this tool could help provide a second opinion to an ophthalmologist about the severity level of DR.