An Interpretable CNN Framework for Accurate Breast Cancer Diagnosis in Medical Imaging

Abstract

Explainable Deep Learning in Breast Cancer Detection is devoted to the creation of smart diagnostic systems, the ones that will be both highly predictive and easy to understand and interpret the medical decisions. Breast cancer has been one of the predominant cancers in women across all continents and the number one cause of death. The importance of early detection as introduced by the World Health Organization is a key factor in enhancing survival and treatment outcomes. Convolutional Neural Networks (CNNs) have demonstrated exceptional results in medical image classification, especially in mammography and histopathological images, with the development of artificial intelligence. The models can automatically extract complex patterns and hierarchical features on raw data thus making it unnecessary to extract each feature manually. Nevertheless, their accuracy is usually high, but CNNs are frequently black-box systems, which is why a clinician cannot easily interpret the logic behind their predictions, which limits the willingness of a clinician to trust it and casts ethical doubts. To overcome this problem, the suggested study presents a explainable deep learning model, which is a combination of CNN-based classification and Explainable Artificial Intelligence (XAI) methodology. Not only is it created to categorize breast tumors as benign or malignant, but it is also aimed at giving clear and interpretable explanations of its predictions. The methods integrating Grad-CAM, SHAP, and LIME are used to produce visual heatmaps and scores of feature importance, allowing radiologists to understand whether the model is paying attention to clinically important areas or not. Key performance metrics such as accuracy, sensitivity, specificity, precision, recall and F1-score are used to evaluate the framework to ensure its reliability of use in medical diagnosis. This method can improve trust and benefit clinical decision-making by incorporating a high-performance deep learning algorithm and a transparent explanation algorithm, which will encourage the responsible use of AI in healthcare.