LAUSR

Abstract The extensive growth of data in the health domain has increased the utility of Deep Learning in health. Deep learning is a highly advanced successor of artificial neural networks, having powerful computing ability. Due to the availability of fast data storage and hardware parallelism its popularity grows in the last five years. This in article presents a comprehensive literature review of research deploying deep learning medical imaging and medical NLP including tasks, pipelines, and challenges. In this work, we have presented an extensive survey of deep learning architecture deployed in the fields of medical imaging and medical natural language processing. This paper helps in identifying suitable combination of Deep learning, Natural language processing and medical imaging to enhance diagnosis. We have highlighted the major challenges in deploying deep learning in medical imaging and medical natural language processing. All the results are presented in pictorial form. This survey is very helpful for novices working in the area of health informatics.