Deep Learning Methods for Diagnosing Thyroid Cancer

Gurmanik Kaur Mann, R. Busi, Satyanarayana Talam, Krishna Marlapalli
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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. 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 applications of deep learning in thyroid cancer imaging and discussed the study's goals. We provided an overview of latest developments in the diagnosis of thyroid cancer using deep learning techniques and addressed about numerous difficulties and practical issues that can restrict the development of deep learning and its incorporation into healthcare setting.
诊断甲状腺癌的深度学习方法
近年来,甲状腺结节成为威胁生命的常见疾病之一。定位和识别甲状腺结节的常用诊断技术是超声波成像。然而,对专家来说,评估所有切片图像既费时又费力。准确评估超声图像需要自动化、可靠和客观的方法。深度学习的最新发展彻底改变了图像分析和计算机辅助诊断(CAD)技术的多个方面,从而解决了甲状腺结节的识别问题。我们回顾了有关深度学习在甲状腺癌成像中的潜力、限制和当前应用的文献,并讨论了本研究的目标。我们概述了使用深度学习技术诊断甲状腺癌的最新进展,并讨论了可能限制深度学习的发展及其融入医疗环境的众多困难和实际问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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