Abstract:
Breast cancer is the most commonly diagnosed cancer worldwide and a leading cause
of cancer-related deaths among women. Early detection remains the most effective
way to improve patient outcomes, and mammography is widely considered the gold
standard for screening. However, interpreting mammograms, particularly in women
with dense breast tissue, presents significant challenges. This study aims to assist in
addressing these limitations by developing a simple web application that integrates a
deep learning model with explainable artificial intelligence (XAI), serving as a clinical
decision support tool for clinicians and medical students. Twelve models were
trained using the INBreast and CBIS-DDSM datasets, combining architectures such
as ResNet50, DenseNet121, EfficientNetV2-B0, and ConvNeXt with optimizers including
SGD, Adam, and AdaMax. Among these, the DenseNet121 model optimized
with Adam delivered the best performance, achieving an F2 score of 67.15%, recall of
72.89%, AUC of 73.54%, precision of 51.05%, and accuracy of 63.49%. Adaptive optimizers
consistently outperformed SGD, with Adam slightly outperforming AdaMax.
Grad-CAM visualizations indicated that the model generally focused on clinically
relevant areas, although occasional misalignment suggests the need for further refinement.
The system shows potential as a clinical decision support tool in mammogram
interpretation. It is intended to aid, but not replace, clinical judgment and should be
used with caution.
