Medical images are being used on a large scale to detect the presence of abnormalities within the human body. Because of the expert's limited subjectivity and fuzziness in medical images, diagnosis and prediction are among the most challenging tasks in medical science. As a result, different professional's assessments of the severity of various diseases may result in incorrect conclusions. To deal with the uncertainties present in the medical data and for the help of medical experts in early and accurate diagnosis, different researchers have applied a logic known as neutrosophic logic in the field of medical science. This study presents a systematic review of research on the applications of neutrosophic logic in biomedical image processing. This work reviews 37 research papers published in prominent journals and conferences during the last ten years by exploring different digital libraries, including IEEE Xplore, Science Direct, Wiley, Springer, and ACM digital libraries. This research helps to understand how neutrosophic logic is superior to previous versions, its current uses in medical image processing, and its future potential in this field.

Medical images are being used on a large scale to detect the presence of abnormalities within the human body.