Abstract

One of the prevalent, life-threatening disorders that have been on the rise in recent years is thyroid nodule. A frequent diagnostic technique for locating and identifying thyroid nodules is ultrasound imaging. Yet, it takes time and presents difficulties for the professionals to evaluate all of the slide photos. Automated, dependable, and objective methods are required for accurately evaluating ultrasound pictures. Recent developments in deep learning have completely changed several facets of image analysis and computer-aided diagnostic (CAD) programmes that deal with the issue of identifying thyroid nodules. We reviewed the literature on the potential, constraints, and present applications of deep learning in thyroid cancer imaging and discussed the study’s goals.