An Efficient Explainable AI-Based Model for Assisting Medical Practitioners in Cancer Detection

Abstract

The implementation of Artificial Intelligence (AI) in healthcare diagnostics represents a significant transition towards more advanced and presumably more efficacious medical methodologies. The incorporation of AI into standard clinical diagnostics presents significant problems, mostly due to the inherent lack of transparency in the machine learning algorithms that underpin these systems. Although AI's ability to analyze intricate datasets surpasses human capabilities, its operations remain mostly opaque to end-users, presenting a substantial obstacle in situations where clinical accountability and patient safety are critical. In cancer research, early and precise cancer detection significantly impacts treatment outcomes and patient survival rates. Consequently, medical practitioners necessitate a comprehensive comprehension of the diagnostic procedures and rationales underlying AI-generated results to fully trust and depend on these technologies. The necessity for transparency and responsibility is exacerbated by the ethical concerns associated with utilizing obscure 'black box' models that offer no or no understanding of their decision-making mechanisms. The medical community's resistance to completely adopting AI solutions arises from insufficient transparency, apprehensions regarding potential biases, the applicability of trained models across varied patient demographics, and the legal implications of AI-generated choices. These complex issues highlight the pressing necessity for the creation of AI systems that are both technically adept and entirely interpretable and explainable to users in critical care situations. This thesis addresses the urgent requirement for transparency and dependability in AI-driven diagnostics by developing sophisticated hybrid deep-learning methodologies that incorporate interpretability into the cancer detection process. The proposed strategy utilizes advanced machine and deep learning methodologies in conjunction with numerous explainable AI techniques, such as SHapley Additive exPlanations (SHAP), to guarantee that each diagnostic prediction is both precise and thoroughly transparent. These strategies elucidate the reasoning underlying the AI's decisions, enabling medical practitioners to discern the features and elements impacting the outcomes, thereby bridging the divide between AI capabilities and human comprehension. The validation of these models across various cancer datasets illustrates their stability and adaptability, with findings indicating that the explainability components improve diagnostic accuracy by facilitating more nuanced interpretations of intricate data patterns. Furthermore, the model's elucidations are tailored to be comprehensible to medical professionals without necessitating extensive technical expertise in AI, which is essential for cultivating trust and assurance. By offering transparent and comprehensible insights into its decision-making processes, the model not only complies with clinical criteria for diagnostic tools but also upholds ethical norms that emphasize patient safety and informed clinical decision-making. This substantial improvement in AI openness alleviates the natural skepticism associated with opaque 'black box' technology, ultimately promoting broader acceptance of AI tools in the vital domain of cancer detection.