Revolutionizing breast cancer diagnosis with a comprehensive approach using digital mammogram-based feature extraction and selection for early-stage identification

Cancer of the breasts is a prevalent and possibly fatal disease that causes abnormal development of cells in breast tissue. It is the most prevalent tumor in women globally, and it has various subtypes that respond differently to treatments. Early detection, such as mammograms, is critical to enhancing outcomes since it allows for prompt intervention. Treatment options may include chemotherapy, radiation therapy, surgery, and hormone therapy, either alone or in combination, depending on the features and stage of the cancer. Breast cancer has profound emotional and psychological effects on individuals and their families in addition to its physical effects Ongoing research, public awareness campaigns, and advances in personalized medicine all contribute to the collective efforts aimed at lowering the incidence of breast cancer, improving early detection, and improving the overall quality of life for those affected. By seamlessly integrating state-of-the-art deep learning models: pre-trained ResNet and U-Net, this study pioneers a transformative approach to breast cancer diagnosis. ResNet's expertise in hierarchical feature learning is combined with U-Net's segmentation prowess to focus on digital mammogram-based feature extraction and early-stage identification. The collaborative synergy provides a solid foundation for the accurate detection of breast abnormalities. A neural network is introduced to augment this process for classification, raising diagnostic capabilities to new heights. The combination of pre-trained ResNet and U-Net models creates a dynamic feature extraction pipeline for capturing intricate patterns and segmenting region-specific abnormalities. This collaborative methodology enables the model to detect subtle nuances indicative of early-stage breast cancer, thereby facilitating early detection and intervention. The seamless integration of these models addresses the complexities of mammographic data, providing a complete solution for accurate and nuanced breast cancer detection. A neural network is added to the diagnostic pipeline for more precise classification. The neural network refines the diagnostic process by analyzing the extracted features, reducing false positives, and increasing specificity. This multi-layered approach represents a significant step forward in breast cancer diagnosis, providing a comprehensive tool that integrates feature extraction, early-stage identification, and classification, with the potential to transform clinical practices and improve patient outcomes. Extensive validation and clinical testing validate this transformative model's efficacy and reliability in real-world healthcare scenarios. The responsible use of this transformative tool is supported by ethical considerations such as patient privacy safeguards and adherence to informed consent principles. This study, as a pioneering effort in breast cancer diagnosis, achieves outstanding performance with an accuracy of 99%, precision of 98.6%, recall of 99.01%, and specificity of 98.9%, showcasing superior metrics, lays the groundwork for future innovations, encouraging improved accuracy, personalized treatment strategies, and, ultimately, improved healthcare outcomes for patients.