This research presents a deep learning-powered breast cancer diagnosis system that improves detection and classification of breast lesions. The system leverages advanced computer vision techniques and deep learning models to enhance the accuracy and efficiency of breast cancer diagnosis. The proposed framework integrates YOLO detection, segmentation using Associated-ResUNets, and classification through AlexNet (BreastNet-SVM) to achieve high performance in identifying and categorizing breast masses and calcifications. The system was validated using the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM), demonstrating exceptional performance with a 99% success rate in detecting and classifying breast masses. The detection accuracy reached 98.5%, while the segmentation accuracy was approximately 95.39%. The classification phase achieved an overall accuracy of 99.16%. The system's ability to accurately identify and classify breast lesions, including architectural distortion, highlights its potential to outperform current deep learning techniques. The proposed framework addresses the challenges of breast cancer diagnosis by improving the accuracy of mammography-based detection and classification. It incorporates data augmentation techniques, skip connections, and advanced neural network architectures to enhance performance. The system's ability to handle complex breast imaging data and provide reliable classifications makes it a valuable tool for medical professionals in breast cancer diagnosis. The study also highlights the importance of early detection and the role of deep learning in improving diagnostic accuracy. The proposed framework demonstrates the effectiveness of combining computer vision and deep learning techniques in breast cancer diagnosis, offering a promising solution for early detection and treatment planning. The results indicate that the system can significantly enhance the accuracy and efficiency of breast cancer diagnosis, providing a reliable tool for healthcare practitioners.This research presents a deep learning-powered breast cancer diagnosis system that improves detection and classification of breast lesions. The system leverages advanced computer vision techniques and deep learning models to enhance the accuracy and efficiency of breast cancer diagnosis. The proposed framework integrates YOLO detection, segmentation using Associated-ResUNets, and classification through AlexNet (BreastNet-SVM) to achieve high performance in identifying and categorizing breast masses and calcifications. The system was validated using the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM), demonstrating exceptional performance with a 99% success rate in detecting and classifying breast masses. The detection accuracy reached 98.5%, while the segmentation accuracy was approximately 95.39%. The classification phase achieved an overall accuracy of 99.16%. The system's ability to accurately identify and classify breast lesions, including architectural distortion, highlights its potential to outperform current deep learning techniques. The proposed framework addresses the challenges of breast cancer diagnosis by improving the accuracy of mammography-based detection and classification. It incorporates data augmentation techniques, skip connections, and advanced neural network architectures to enhance performance. The system's ability to handle complex breast imaging data and provide reliable classifications makes it a valuable tool for medical professionals in breast cancer diagnosis. The study also highlights the importance of early detection and the role of deep learning in improving diagnostic accuracy. The proposed framework demonstrates the effectiveness of combining computer vision and deep learning techniques in breast cancer diagnosis, offering a promising solution for early detection and treatment planning. The results indicate that the system can significantly enhance the accuracy and efficiency of breast cancer diagnosis, providing a reliable tool for healthcare practitioners.