ADVANCED CNN-BASED FRAMEWORKS FOR ROBUST AND EXPLAINABLE BREAST CANCER DIAGNOSIS ACROSS MULTI-MODAL IMAGING DATASETS

Breast cancer stands as the principal cause triggering female fatalities worldwide hence requiring immediate diagnostic solutions which produce precise and timely results. Expanding from its demonstrated potential deep learning technologies function with constraints stemming from single-type medical image analysis as well as inadequate transparency regarding decision-making operations. This research examines deficient diagnostic methods because they fail to effectively link multimodal medical images with healthcare parameters to diagnose breast cancer both precisely and explainable to medical staff. The predictive model uses a combination of mammograms, ultrasounds and MRIs together with histopathological images and structured clinical data features like age, breast density, lesion size, genetic marker scores and tumor stage to achieve better predictive results and stronger model generalization. This study eliminates the present knowledge gaps through a novel Multi-Modal Explainable Convolutional Neural Network (MME-CNN) framework that unites mammograms with MRIs and ultrasounds, histopathological images and structured clinical data containing age, lesion size, breast density, genetic markers and tumor stage information. Grad-CAM visualizations served within the model as an interpretability tool that shows doctors how the predictions were made. The experimental analysis shows the model achieved a perfect validation accuracy of 100% while needing only four epochs to complete training. It reduced training loss from 0.6975 to 0.2551 and established a validation loss at 0.0886. Real-world clinical implementations benefit from this framework because it shows good universal applicability and quick calculation rates and better explanation capabilities. The future development will concentrate on conducting extensive validity tests alongside EHR system combinations to enable widespread precision oncology implementation.