Breast cancer detection and classification using deep learning techniques based on ultrasound image

Breast cancer ranks as the most prevalent form of cancer diagnosed in women. Diagnosis faces several challenges, such as changes in the size, shape, and appearance of the breast, dense breast tissue, and lumps or thickening, especially if present in only one breast. The major challenge in the deep learning (DL) diagnosis of breast cancer is its non-uniform shape, size, and position, particularly with malignant tumors. Researchers strive through computer-aided diagnosis (CAD) systems and other methods to assist in detecting and classifying tumor types. This work proposes a DL system for analyzing medical images that improves the accuracy of breast cancer detection and classification from ultrasound (US) images. It reaches an accuracy of 99.29%, exceeding previous work. First, image processing is applied to enhance the quality of input images. Second, image segmentation is performed using the U-Net architecture. Third, many features are extracted using Mobilenet. Finally, classification is performed using visual geometry group 16 (VGG16). The accuracy of detection and classification using the proposed system was evaluated.