Medical imaging involves capturing images of internal organs to aid in diagnosing and treating illnesses. Enhancing the effectiveness of clinical research and treatment choices is the main goal of medical image analysis. Deep learning (DL) has produced outstanding outcomes for image processing tasks such feature extraction, segmentation, augmentation, and classification, significantly altering the field of medical image analysis. DL methods are helpful in helping medical professionals identify the correct diagnosis by uncovering hidden patterns in photos. Numerous deep learning techniques have been reported to be used in the analysis of medical images to support different types of diagnosis. It has shown to be the most successful technique for cancer detection, organ segmentation, computer-assisted diagnosis, and disease classification. In this paper, we review the literature that uses DL methods and how they are applied to medical imaging for processing medical images.