Automated Detection and Segmentation of Breast Cancer in Ultrasound Images with Deep Learning

Breast cancer, characterized by uncontrolled cell division in breast tissue, affects individuals of all genders. Risk factors encompass age, genetics, hormones, and lifestyle. Symptoms comprise lumps, breast and nipple changes, and skin abnormalities. Early detection via self-exams, clinical exams, and mammograms is crucial. Artificial Intelligence (AI) in breast cancer detection systems utilizes advanced algorithms and image analysis techniques to aid radiologists in identifying potential cancerous abnormalities, enhancing accuracy, and facilitating earlier diagnosis. In this paper, various feature fusion methods were proposed for binary classification of breast cancer. The first technique involves concatenating the flattened layers of VGG19 and Xception models. The second technique removes the fully connected layers of both VGG19 and Xception models, concatenates their outputs, and applies an Encoder to reduce spatial dimensions and extract hierarchical features. Both Flatten and GlobalAveragePooling2D (GAP2D) are utilized to convert a 2D feature map into a 1D vector and choose the best ones. Our approach achieved the best results with an accuracy of 94.61% for classification and a dice score of 72% for segmentation task.