Multimodal image fusion for the detection of diabetic retinopathy using optimized explainable AI-based Light GBM classifier

Abstract

Diabetic Retinopathy (DR) is a widespread ocular condition and a significant contributor to global blindness. Timely identification and precise diagnosis of DR are essential for successfully managing and avoiding vision impairment. Visualizing and studying the intricate vascular network and other retinal structures has notable difficulties due to several factors that complicate the process. To overcome this, the proposed Dunnock-Scheduler optimization-based Light GBM (DkSO-Light GBM) is introduced for multimodal image fusion for DR detection. This approach can assist clinicians in making informed decisions, identifying essential features, and ensuring transparency in the automated DR diagnosis process. In this research, the Optical Coherence Tomography Angiography (OCTA) images undergo feature map generation using ResNet 101, and the DkSO algorithm is used by the AI-based Light Gradient Boosting Machine (GBM) to classify the normal and abnormal retina. The DkSO algorithm relies on the searching and scheduling characteristics, which enhance the model to identify DR more accurately by fusion of OCTA and fundus images. The experimental outcomes illustrate that the accuracy, sensitivity, specificity, precision, F1 score, balanced accuracy, and Mathews Correlation Coefficient (MCC) of the DkSO-Light GBM are 94.32 %, 94.94 %, 94.78 %, 94.78 %, 94.25 %, 94.86 %, and 91.77 % respectively, at a Training percentage (TP) of 90. In terms of k-fold 6, the metrics stand at 95.53 %, 94.72 %, 95.41 %, 94.16 %, 93.83 %, 95.07 %, and 92.00 %, respectively, signifying the superior efficiency of the DkSO-Light GBM model compared to other conventional techniques.