Medical Image analysis is important in monitoring the clinical amelioration of various diseases and determining the patient response to a specific treatment. In medical image analysis, deep learning is regarded as the state-of-the-art technology to support medical practitioners and professionals by ensuring better clinical decisions in the clinical workflow.

With the expansion of deep learning, medical image analysis has become a dynamic research area. Deep learning techniques have potential benefits with extraordinary capabilities making the diagnosis, prediction, and detection more precise and rapid by analyzing medical images.

Deep learning-based medical image analysis provides decision assistance to clinicians and enhances the accuracy and effectiveness of various diagnostic and treatment processes. Deep learning architectures utilized for medical image analysis are 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).

Prominent medical imaging modalities used for deep learning-based medical image analysis are Computed Tomography, Magnetic Resonance Imaging, X-rays, Positron Emission Tomography, Histopathology, Ultrasound, Colonoscopy, and Dermoscopy. Deep learning-based pattern recognition processes in medical image analysis are medical image registration, medical image segmentation, medical image localization, medical image classification, and medical image detection.

Deep learning-based medical image analysis is applied to analyze anatomical organs such as the eye, chest, brain, abdomen, breast, kidney, lungs, liver, heart, and skin. Lack of appropriately annotated data, imbalanced data, and confidence interval are common challenges in deep learning-based medical image analysis. Deep learning approaches in medical image analysis are enumerated below;

Detection/Localization - Deep learning models identify a specific region of interest in medical images via computer-aided detection, which is beneficial to predict the earliest signs of deformity in patients. The anatomic site detected using deep learning models is the abdomen, chest, eye, breast, brain, and lung. Mostly, a convolutional neural network is used for deep learning-based disease detection.

Segmentation - In medical image analysis, image segmentation is performed using deep learning techniques with different image modalities such as Computed Tomography, X-ray, Positron Emission Tomography, Ultrasound, Magnetic Resonance Imaging, and Optical Coherence Tomography (OCT). Deep learning-based image segmentation provides the segmentation labels for the diseased region of interest in the breast, brain, kidney, eye, and liver.

Classification - Deep learning models perform medical image classification to identify the disease in the anatomic site, such as the chest, brain, head, and abdomen. Convolutional neural networks and auto-encoders are commonly used for deep learning-based medical image classification. Medical image registration, content-based image retrieval, image generation and enhancement, and combining image data with reports are deep learning uses in medical image analysis.