A Survey on Medical Image Analysis using Deep Learning Techniques
Deep learning algorithms, in particular convolution networks, have rapidly become a methodology of choice for analyzing medical images. Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to analyze medical images. This survey reviews the major deep learning concepts pertinent to medical image analysis and explains the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. This survey also discusses about different databases used for medical image analysis. Finally, it discusses briefly about issues and research challenges faced by researchers along with future direction.