  Machine learning (ML) has seen enormous consideration during the most recent
decade. This success started in 2012 when an ML model accomplished a remarkable
triumph in the ImageNet Classification, the world's most famous competition for
computer vision. This model was a kind of convolutional neural system (CNN)
called deep learning (DL). Since then, researchers have started to participate
efficiently in DL's fastest developing area of research. These days, DL systems
are cutting-edge ML systems spanning a broad range of disciplines, from human
language processing to video analysis, and commonly used in the scholarly world
and enterprise sector. Recent advances can bring tremendous improvement to the
medical field. Improved and innovative methods for data processing, image
analysis and can significantly improve the diagnostic technologies and
medicinal services gradually. A quick review of current developments with
relevant problems in the field of DL used for medical imaging has been
provided. The primary purposes of the review are four: (i) provide a brief
prolog to DL by discussing different DL models, (ii) review of the DL usage for
medical image analysis (classification, detection, segmentation, and
registration), (iii) review seven main application fields of DL in medical
imaging, (iv) give an initial stage to those keen on adding to the research
area about DL in clinical imaging by providing links of some useful informative
assets, such as freely available DL codes, public datasets Table 7, and medical
imaging competition sources Table 8 and end our survey by outlining distinct
continuous difficulties, lessons learned and future of DL in the field of
medical science.
