EXPLANIABLE DEEP LEARNING FOR THE BREAST CANCER DETECTION BRIDGING ACCURACY AND INTERPRETABILITY

Abstract

Cancer remains one of the leading causes of mortality worldwide, making early and accurate detection essential for improving patient survival rates and treatment outcomes. Recent advancements in artificial intelligence and machine learning have significantly enhanced the capability of automated cancer detection systems. However, many highly accurate deep learning models often operate as black-box systems, limiting their interpretability and reducing the trust of healthcare professionals in clinical decision-making. This paper presents a cancer detection framework that bridges accuracy and interpretability by integrating advanced machine learning techniques with explainable artificial intelligence (XAI) methods. The proposed approach analyzes medical data such as imaging scans, histopathological images, and clinical records to accurately identify cancerous conditions while providing transparent explanations for model predictions. By combining high-performance predictive models with interpretability mechanisms, the framework enables clinicians to understand the factors influencing diagnostic decisions. Experimental analysis demonstrates that the proposed system achieves high detection accuracy while maintaining explainability and clinical relevance. The developed framework supports reliable, trustworthy, and efficient cancer diagnosis, thereby enhancing healthcare decision-making and patient care.