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.

Breast cancer still contributes a major portion of the world deaths due to such diseases, and innovative methods to diagnose are needed. There is a lack of novel diagnostic solutions, which present limited accuracy, scalability issues, and lack of interpretability in dealing with complex medical data. This work is designed to improve the system's accuracy, scalability, and interpretability in predicting breast cancer, through an integrated system involving robotic automation, Transformer-guided graph neural networks, hybrid neural models, and fuzzy decision frameworks. The objective is to design a robust platform that can make reliable, real-time predictions relevant to clinical practice. The framework proposed was based on graph neural networks for analyzing relational information, hybrid architectures to extract spatial and temporal patterns, and fuzzy logic in dealing with uncertainty. The robotic processes dealt with the multi-modal datasets, images, and records efficiently and scalably. Benchmarking measured performance against state-of-the-art methods by metrics like precision, recall, and processing speed. The proposed method achieved 95.3% accuracy and 96.7% recall with the processing time reduced to 180 milliseconds, which was better than other models tested. Its high scalability and interpretability validate its applicability to real-world clinical implementations. This reliable scalable prediction platform for breast cancer shall be a game-changing source of innovation through this state-of-the-art fusion of advance techniques.

1. At present time, higher education plays a fundamental part in changing the growth of society (Fernandez-Garcia et al. 2020) (Zayed, Salman, and Hasasneh 2022). In basic terms, the learning strat...