BreastCare application: Moroccan Breast cancer diagnosis through deep learning-based image segmentation and classification

Breast cancer remains a critical health problem worldwide. Increasing survival rates requires early detection. Accurate classification and segmentation are crucial for effective diagnosis and treatment. Although breast imaging modalities offer many advantages for the diagnosis of breast cancer, the interpretation of breast ultrasound images has always been a vital issue for physicians and radiologists due to misdiagnosis. Moreover, detecting cancer at an early stage increases the chances of survival. This article presents two approaches: Attention-DenseUNet for the segmentation task and EfficientNetB7 for the classification task using public datasets: BUSI, UDIAT, BUSC, BUSIS, and STUHospital. These models are proposed in the context of Computer-Aided Diagnosis (CAD) for breast cancer detection. In the first study, we obtained an impressive Dice coefficient for all datasets, with scores of 88.93%, 95.35%, 92.79%, 93.29%, and 94.24%, respectively. In the classification task, we achieved a high accuracy using only four public datasets that include the two classes benign and malignant: BUSI, UDIAT, BUSC, and BUSIS, with an accuracy of 97%, 100%, 99%, and 94%, respectively. Generally, the results show that our proposed methods are considerably better than other state-of-the-art methods, which will undoubtedly help improve cancer diagnosis and reduce the number of false positives. Finally, we used the suggested approaches to create “Moroccan BreastCare”, an advanced breast cancer segmentation and classification software that automatically processes, segments, and classifies breast ultrasound images. • A novel model developed for breast tumor segmentation, addressing the challenges of ultrasound image interpretation in breast cancer diagnosis. • Using EfficientNetB7 to classify breast tumors as benign or malignant, yielding high accuracies. • Results demonstrate significant improvement over state-of-the-art methods, aiding in earlier cancer detection and reducing diagnostic errors and false positives. • A software solution that automates the segmentation and classification of breast ultrasound images, leveraging the proposed models to enhance breast cancer diagnosis capabilities.