Towards Reliable and Fair Deep Learning for Skin Cancer Screening: An Explainable, Data-Aware Framework

Deep learning has become a useful research direction for computer aided analysis of skin lesions, but strong results on a single dataset do not necessarily translate into reliable clinical performance. This paper proposes a practical framework for skin cancer screening research that treats image quality, lesion localization, classification, uncertainty, explanation, and external validation as connected parts of one workflow. The framework is developed through a synthesis of three supplied review papers: work on skin cancer segmentation and classification, a survey of deep learning for dermatological conditions, and a review of deep learning in cervical cancer diagnosis. The first two sources directly inform the skin imaging discussion, while the cervical cancer review contributes broader lessons about medical image pipelines and the need to validate automated screening systems. Rather than claiming new experimental results, this article presents a research proposal and evaluation plan. It recommends patient-level data separation, balanced reporting across lesion categories and skin tones, comparison with and without segmentation, calibrated uncertainty estimates, explainability checks, and external testing across imaging settings. The central argument is that clinical usefulness should be judged by robustness, fairness, interpretability, and workflow fit alongside conventional classification scores.