GradCAM Analysis Reveals Deep Learning Models Misclassify Benign-Appearing Melanomas Based on Possible Architectural Shortcomings Instead of Misdirection Attention

Melanoma is responsible for the vast majority of skin cancer deaths, making early and accurate detection critically important. While modern deep learning systems often achieve performance comparable to dermatologists, certain melanomas remain especially difficult to identify because they closely resemble benign lesions. In this project, I investigated how artificial intelligence models classify these challenging “benign-appearing” melanomas and used GradCAM analysis to visualize the image regions influencing model decisions. By comparing model performance on typical and atypical melanoma presentations, I explored potential limitations in current deep learning architectures and identified opportunities for improving future AI-assisted diagnostic systems.

• Medicine
• Artificial Intelligence