ABSTRACT
Breast cancer (BC) is one of the most leading malignancies amongst women globally; consequently, one in eight women is infected during the lifespan. The malignancy growth is reported in the breast glandular epithelium. The breast-cancer-infected individuals' prediction would be enhanced due to the required early detection and diagnosis process. Regardless of vast medical evolution, breast cancer has the second primary cause of mortality yet. Hence, initial diagnosis plays a preeminent role in decreasing the mortality rate. Several breast cancer detection procedures include computed tomography, mammography, X-rays, ultrasound, magnetic resonance imaging, thermography and more. Deep learning techniques were commonly used for medical imaging recognition for the past several years. convolutional neural network (CNN) was developed for accurate image recognition and classification, including breast cancer images due to its automatic detection and disease classification. This chapter aims to provide an extensive analysis of breast cancer, abnormalities, diagnosis, treatments and prevention strategies using image processing and CNN techniques. Additionally, available datasets description and critical statistical analysis based on CNN and different modalities for further study and future challenges are also highlighted.
