Mitigating Class Imbalance through Multimodal Fusion in VGG19 for Cataract Detection

Global analysis indicates a notable rise in non- infectious vision impairments, with India seeing a pronounced spike in cases. Numerous international investigations have shown that Indian populations face an elevated likelihood of developing cataracts, glaucoma, and even permanent blindness. This article puts forth a feasible solution for early cataract screening, tapping into state-of-the-art Artificial Intelligence technologies to pinpoint potential cataract affliction. Remarkably, this was accomplished with tremendous computational efficiency—the VGG19 deep neural network model managed to train on a modest dataset of only 870 photographic images, yet still achieved excellent diagnostic performance with 99% accuracy. Though rudimentary, this preliminary AI framework demonstrates considerable promise to assist in cataract risk stratification and prevention efforts on a global scale, with particular utility in high-risk Indian communities. Additional research with more expansive datasets will further refine the precision and generalizability of the model across human populations. But these initial findings provide a crucial first step toward ameliorating worldwide visual morbidity using sophisticated decision-support tools.