Convolutional Neural Networks for Breast Cancer

One of the highly dangerous along with the 2nd major diseases that cause female death is Breast Cancer (BC). The growth of malignant, cancerous lumps as of the breast cells is the beginning stage of BC. It can be diagnosed earlier by self-tests along with regular clinical checks; in that way enhance the survival probability considerably. For researchers along with scientists, BC classification is a highly challenging task. In cancer data classification, Neural Networks have turned into a well-liked tool in recent days. Illustrating the present understanding of Convolutional Neural Networks (CNNs) in BC Detection (BCD) in an organized along with structured format is the intention of this survey. In this, the CNN- centric Transfer Learning (TL) is investigated to portray breast masses for various diagnostic, predictive, or prognostic tasks or in a variety of imaging modalities like Ultrasound (US), Magnetic Resonance Imaging (MRI), Digital Mammography (DM), together with Digital Breast Tomosynthesis (DBT). The recognition of advantages in conjunction with the drawbacks of BCD utilizing CNN is the motive of this work. Lastly, the potential chances for future work are spotlighted in this paper. In this, a road map for developing the CNN-centric solutions to enhance BC further is provided by the materials illustrated along with discussed here.