A Deep Learning and Explainable Artificial Intelligence based Scheme for Breast Cancer Detection.

Incorporating Artificial Intelligence (AI) presents significant potential for transforming multiple aspects of the healthcare sector, encompassing administration, medical prediction, decision-making, and diagnostics. However, its often perceived enigmatic nature is a significant barrier to the widespread use of AI in the medical field. AI systems, specifically deep learning models, have demonstrated exceptional performance, often comparable to that of humans. However, their internal mechanisms sometimes resemble opaque entities, leading to challenges in understanding and placing trust in their decision-making processes. The presence of skepticism has limited their practical application in medical contexts. Given the challenges above, a pioneering proposed scheme, namely "DXAIB", has been introduced. The proposed scheme utilizes a hybrid methodology that integrates Convolutional Neural Networks (CNNs) with the Random Forest (RF) model to detect breast cancer accurately. One distinguishing characteristic of "DXAIB" is its distinctive focus on interpretability of the predictions. The RF technique, employed to predict class labels, implements an automated feature learning process facilitated by the "DXAIB" scheme, which has several convolutional layers. However, "DXAIB" doesn't stop at providing accurate predictions. To tackle the significant concern surrounding the interpretability of AI, an approach called "SHAP" is utilized to offer explanations at both the local and global levels for every prediction. This implies that "DXAIB" not only facilitates the identification of a diagnosis but also provides comprehensive explanations regarding the rationale behind a specific prediction, augmenting transparency and fostering confidence in the decision-making process of the AI system. The proposed "DXAIB" scheme surpasses other cutting-edge schemes in terms of the prediction outcomes it achieves and, therefore, in terms of its explainability findings.