Seeing Isn't Always Believing: Analysis of Grad-CAM Faithfulness and Localization Reliability in Lung Cancer CT Classification
This study evaluates the faithfulness and localization reliability of Grad-CAM for interpreting lung cancer classification in chest CT scans. It presents the first systematic comparison between convolutional architectures (ResNet, DenseNet, EfficientNet) and Vision Transformers (ViT) in terms of explanation consistency, localization accuracy, and robustness to perturbations, thereby elucidating how attention mechanisms influence interpretability. The findings reveal that Grad-CAM yields robust explanations in convolutional models but suffers from distorted visualizations in ViT due to its non-local attention, with significant inter-model discrepancies in localization performance—raising concerns about its clinical generalizability. To address these issues, this work proposes a model-aware interpretability evaluation framework, offering a new perspective toward the trustworthy deployment of medical AI systems.