This research article explores the application of deep learning and computer vision techniques in breast cancer diagnosis, focusing on detection, segmentation, and classification. The study highlights the importance of early detection through mammography screening, which is particularly critical in Asia due to various factors contributing to the high prevalence of breast cancer. To address the limitations of traditional mammography, an innovative Computer-Aided Diagnosis (CAD) system was developed, leveraging deep learning and computer vision techniques. The system independently identifies and categorizes breast lesions, segments mass lesions, and classifies them based on pathology. Thorough validation using the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) dataset demonstrated the CAD system's exceptional performance, achieving a 99% success rate in detecting and classifying breast masses. The detection accuracy was 98.5%, while the segmentation accuracy was approximately 95.39%, and the overall classification accuracy was 99.16%. The study also discusses the potential challenges and future directions, emphasizing the need for further validation with larger and more diverse datasets to ensure the robustness and generalizability of the proposed framework. The integrated framework offers valuable support to researchers and physicians in breast cancer diagnosis by harnessing cutting-edge AI and image processing technologies, extending recent advances in deep learning to the medical domain.This research article explores the application of deep learning and computer vision techniques in breast cancer diagnosis, focusing on detection, segmentation, and classification. The study highlights the importance of early detection through mammography screening, which is particularly critical in Asia due to various factors contributing to the high prevalence of breast cancer. To address the limitations of traditional mammography, an innovative Computer-Aided Diagnosis (CAD) system was developed, leveraging deep learning and computer vision techniques. The system independently identifies and categorizes breast lesions, segments mass lesions, and classifies them based on pathology. Thorough validation using the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) dataset demonstrated the CAD system's exceptional performance, achieving a 99% success rate in detecting and classifying breast masses. The detection accuracy was 98.5%, while the segmentation accuracy was approximately 95.39%, and the overall classification accuracy was 99.16%. The study also discusses the potential challenges and future directions, emphasizing the need for further validation with larger and more diverse datasets to ensure the robustness and generalizability of the proposed framework. The integrated framework offers valuable support to researchers and physicians in breast cancer diagnosis by harnessing cutting-edge AI and image processing technologies, extending recent advances in deep learning to the medical domain.