With ongoing artificial intelligence developments, deep learning is effectively utilized in numerous applications, particularly in healthcare. In medical image analysis, deep learning technology helps identify, classify, segment, and measure patterns in clinical images.

Deep learning techniques effectively assist medical practitioners and experts by interpreting medical images for better clinical decision-making. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Auto-encoders (AEs) and Stacked Auto-encoders (SAEs), Restricted Boltzmann Machines (RBMs), Deep Belief Networks (DBNs), Restricted Boltzmann Machines (RBMs) and Deep Belief Networks (DBNs) are the deep learning architectures utilized for medical image analysis. Popular medical imaging modalities utilized for deep learning-based medical image analysis are Computed Tomography, Magnetic Resonance Imaging, X-rays, Positron Emission Tomography, Ultrasound, Colonoscopy, Histopathology, and Dermoscopy. The eye, chest, brain, abdomen, breast, kidney, lungs, liver, heart, and skin are anatomical organs that can be analyzed using deep learning-based medical image analysis.

Medical image registration, medical image segmentation, medical image localization, classification, and medical image detection are deep learning-based methodological tasks in medical image analysis.

Numerous literature surveys and reviews have been published that presents more beneficial details such as deep learning approaches, medical image analysis concepts, trends, challenges, medical image preprocessing methods, applications area, and promising future directions.