Interpretable AI for Skin Lesion Detection: Enhancing Diagnostic Accuracy with CNN and Score-CAM in IoMT Systems
Year: 2025
Type: article
Abstract: An AI-powered system for detecting skin lesions integrates IoMT platforms with CNN for feature extraction and Score-CAM for enhanced interpretability. The primary aim is to develop a system that accurately classifies skin lesions while providing visual justifications to boost clinician confidence, particularly in regions with limited dermatological resources. The system employs CNN-based feature extraction on dermoscopic images pre-processed using BM3D for noise reduction and CLAHE for contrast enhancement. Score-CAM integration improves model interpretability, while IoMT platforms ensure robustness and enable real-time diagnostics across diverse skin tones and lesion types. The proposed approach outperformed previous methods, achieving 99.20% accuracy, with real-time diagnostic capabilities offering significant benefits for remote and underserved areas. The model enhances accessibility, accuracy, and interpretability, making it highly beneficial for diverse populations.
Authors: Rahul Jadon, Venkata Surya Teja Gollapalli, Kannan Srinivasan, Guman Singh Chauhan, Rajababu Budda
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4407879791