Blockchain Integration for Multi-Skin Lesion Classification Advancing Diagnostic Security

Skin cancer, such as melanoma, basal cell carcinoma, squamous cell carcinoma, and dermatofibroma, is a worldwide health problem with its increasing incidence and high mortality rates. The conventional diagnosis techniques, i.e., visual inspection and biopsy, are time-consuming and result in late diagnosis. In this paper, an end-to-end deep learning system using Convolutional Neural Networks (CNNs) is presented to enhance early detection and classification of different types of skin lesions. A complete dermoscopic image dataset is preprocessed, e.g., augmentation and normalization, to improve the generalization ability of the model. The presented system integrates image enhancement, feature extraction, and model training with complete performance evaluation by accuracy, sensitivity, specificity, and AUC-ROC values. To ensure data security and privacy compliance, federated learning is incorporated to enable decentralized training without compromising patient confidentiality. Blockchain implementation ensures improved data integrity and traceability, ensuring transparency of patient records. Explainable AI (XAI) techniques are employed to ensure reliability and interpretability in clinical decision-making. The experiment results indicate the improved classification accuracy of the CNN model compared to conventional approaches, reducing false positives and improving diagnostic efficiency. This research illustrates the potential of AI-based dermatological diagnosis to offer a scalable, non-invasive, and secure screening platform, ultimately leading to improved early detection and patient outcomes while ensuring ethical and regulatory compliance.