Authors - Vipin Bansal, Manisha Malhotra Abstract - Artificial Intelligence (AI) is transforming industries such as automotive, healthcare, insurance, and manufacturing through computer vision and Convolutional Neural Networks (CNNs) for image analysis. In medical imaging, AI enhances the interpretation of MRI, X-rays, and CT scans, reducing human error and improving diagnostic efficiency. Early detection of Diabetic Retinopathy (DR), a severe diabetes complication, is crucial to preventing vision loss. Traditionally, ophthalmologists manually analyzed retinal images to detect abnormalities like fluid leaks or lesions. AI now enables more precise and efficient analysis. This paper presents ViT-MADv2, an improved method for detecting DR-related abnormalities using Vision Transformer (ViT) generative models. As an extension of previous research [1], ViT-MADv2 enhances the base image generation module by incorporating diverse training data with variations in contrast, color, and lesion size. It also refines the similarity evaluator module to improve analysis. The model leverages a novel approach to compare embeddings from original and generated images, identifying DR-specific patterns. Experimental results demonstrate a 2% accuracy gain, reaching 96.5%, with improved sensitivity—a crucial factor in healthcare. These advancements strengthen AI-driven diagnostics, enhancing clinical confidence. Source code: https://github.com/vipinbansal1/vitmadv2.
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