Challenges of Medical Text and Image Processing

The processing of text and images has experienced expansion, and has been such a research area which attracts expertise from various domains, for example, medical, mathematics and computer. The advancement of Machine Learning and Deep Learning models has positively affected the medical domain. Computer processing with the help of diagnostics has already become a foremost part of clinical routine. As the use of technology advances, greater challenges appear. Some examples are the processing and analysis of a significant volume of images, the inadequacy of annotated data and the unavailability of algorithms on public domains for the use of information in the disease detection process. Other challenges include the suspicion of the community on the automatic technology and lack of awareness in the community. All these challenges need to be overcome for the successful implementation of medical applications. This paper discusses the challenges of medical text and image processing and emphasizes the need to conquer these challenges for the betterment of the community. The principal objective of this research is to discuss the challenges in medical text and image processing amongst the public and to promote interest in further research in this field.