ESIDLPD: design of an efficient exudate statistics-based incremental deep learning model to detect progression of diabetic retinopathy

Diabetic retinopathy (DR) is a leading cause of blindness in diabetic patients, emphasising the need for early detection and efficient classification techniques. Existing methodologies focus on conventional deep learning models that often struggle with incremental learning and may overlook crucial exudate-based features. These models face challenges in accurately segmenting complex features in fundus images, leading to sub-optimal classification results. We propose an innovative exudate statistics-based incremental deep learning model (ESIDLPD) to overcome these limitations. Utilising fundus images, the model accurately extracts a wide range of exudates and segments complicated features using the UNet architecture. Segmented blocks are transformed into multi-domain features and are classified into DR classes through an LSTM-based RNN process, supplemented with a VARMAx process for enhanced prediction. This approach refines categorisation, improving precision by 4.5%, accuracy by 3.5%, recall by 1.9%, AUC by 2.5%, and specificity by 1.5%, while reducing diagnostic delay by 10.4%.