ABSTRACT
The emergence of deep learning (DL) has the power to transform the entire picture of healthcare, and it has been used diligently to detect diseases. Medical imaging is one of those areas where a set of techniques create visual representations of the internal parts of the body. By using deep learning, one can do detection and prediction effectively and efficiently. In the healthcare domain, there is a need to locate anomalies and recognize specific indications of different diseases. This chapter focuses on analytic methods that have used deep-learning techniques in medical imaging. The chapter also covers the most common challenges incurred in deep learning based medical imaging solutions and suggests possible solutions. It emphasizes how deep learning with image classification and segmentation is applied in medical imaging. Further, a case study of cardiovascular disease detection using a convolution neural network along with some existing works in the literature is presented. The chapter serves as a basic building block for deep learning in medical applications, and it offers viewpoints to further investigate the domain over more real-world problems. It also highlights the future scope and research directions in this area.
