Advancing AI Interpretability in Medical Imaging: A Comparative Analysis of Pixel-Level Interpretability and Grad-CAM Models

This study introduces the Pixel-Level Interpretability (PLI) model, a novel framework designed to address critical limitations in medical imaging diagnostics by enhancing model transparency and diagnostic accuracy. The primary objective is to evaluate PLI’s performance against Gradient-Weighted Class Activation Mapping (Grad-CAM) and achieve fine-grained interpretability and improved localization precision. The methodology leverages the VGG19 convolutional neural network architecture and utilizes three publicly available COVID-19 chest radiograph datasets, consisting of over 1000 labeled images, which were preprocessed through resizing, normalization, and augmentation to ensure robustness and generalizability.…