Interpretable Ai Techniques For Classifying Eye Diseases Using Gradient-Based Methods

Abstract

An AI-based eye disease classification system provides with gradient-based methods and InceptionV3 for more accurate diagnosis and transparency. The InceptionV3 model trained on retinal images is able to accurately classify the types of diseases such as diabetic retinopathy, glaucoma and age related macular degeneration (AMD). Gradient based techniques such as Grad-CAM, Integrated Gradients and SmoothGrad identify key zones in the retina, providing interpretable visual reasoning of model’s calls. This increases clinician trust and helps make more specific diagnoses by detecting lesions, blood vessels and optic disc abnormalities. The system combines an Arduino microcontroller and an LCD for showing AI generated heat maps, severity and disease classification results in real time. The clinician is alarmed by the sound of buzzer upon the occurrence of critical stages; the loudest retinal noises that need urgent medical care. The mobile and affordable design guarantees access to the clinical case as well as remote healthcare scenarios, and simplifies AI-powered disease detection. Such challenges are balancing accuracy against interpretation and the optimization of Arduino-LCD integration for a smooth visualization. However, model optimizations guarantee that high accuracy, real-time performance, and smooth hardware-software interaction are provided. This system closes the gap between AI and clinical practice through providing real-time explainable insights, allowing the clinician to make better decision and serve the patient better.