This book introduces the fundamentals of Deep Learning for biomedical image analysis for applications including ophthalmology, cancer detection and heart disease. The book discusses multimedia data analysis algorithms and the principles of feature selection, optimisation and analysis. In the whole world, people are suffering from a variety of diseases. In order to detect these diseases, several medical imaging procedures are used in which images from different parts of the body are captured through advanced sensors and well-designed machines. These medical imaging procedures increase the expectations of patients in achieving better healthcare services from medical experts. Till now, various image processing algorithms such as neural networks (NN), convolutional neural networks (CNN), and Deep Learning are used for image analysis, image representation, and image segmentation. Yet, these approaches are not giving promising results in some applications of the healthcare sector. So, this chapter gives an overview of state-of-the-art image processing algorithms as well as highlights its limitations. Most Deep Learning algorithm implementations focus on the images of digital histopathology, computerized tomography, mammography, and X-rays. This work offers a thorough analysis of the literature on the classification, detection, and segmentation of medical image data. This review aids the researchers in considering necessary adjustments to Deep Learning algorithm-based medical image analysis. Further, the applications of medical image processing using Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning in the healthcare sector are discussed in this book.