Deep Learning Methods for Diagnosing Thyroid Cancer

Abstract

Abstract One of the prevalent, life-threatening disorders that has been on the rise in recent years is thyroid nodule. A frequent diagnostic technique for locating and identifying thyroid nodules is ultrasound imaging. However, it takes time and presents difficulties for the specialists to evaluate all of the slide images. Automated, reliable, and objective methods are required for accurately evaluating ultrasound images. Recent developments in deep learning have completely changed several facets of image analysis and computer-aided diagnostic (CAD) techniques that deal with the issue of identifying thyroid nodules. We reviewed the literature on the potential, constraints, and present deep learning applications for thyroid cancer detection and discussed the study's goals. We provided an overview of latest developments in the deep learning techniques for thyroid cancer diagnosis and addressed some of the difficulties and practical issues that can restrict the development of deep learning and its incorporation into healthcare setting.