Review of Deep Learning Approaches for Thyroid Cancer Diagnosis

Abstract

Thyroid nodule is one of the common life-threatening diseases, and it had an increasing trend over the last years. Ultrasound imaging is a commonly used diagnostic method for detecting and characterizing thyroid nodules. However, assessing the entire slide images is time-consuming and challenging for the experts. For assessing ultrasound images in a meaningful manner, there is a need for automated, trustworthy, and objective approaches. The recent advancements in deep learning have revolutionized many aspects of computer-aided diagnosis (CAD) and image analysis tools that address the problem of diagnosing thyroid nodules. In this study, we explained the objectives of deep learning in thyroid cancer imaging and conducted a literature review on its potential, limits, and current application in this area. We gave an overview of recent progress in thyroid cancer diagnosis using deep learning methods and discussed various challenges and practical problems that might limit the growth of deep learning and its integration into clinical workflow.

The role of blockchain technology in advancing sustainable energy with security settlement: enhancing security and efficiency in China’s security market

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Deep learning health management diagnostics applied to the NIST smoke experiments

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Advances of AI in Image-Based Computer-Aided Diagnosis: A Review

“…Ranjbarzadeh et al [31] demonstrated that AI methods and algorithms are frequently used to identify brain tumors, calculate tissue volumes, and support computer-aided surgery and treatment planning. Anari et al [32] stated that the diagnosis of thyroid cancer by computer-aided diagnosis (CAD) and image analysis techniques has been transformed by deep learning. Deep learning models can help diagnose and treat thyroid nodules, giving medical professionals significant assistance.…”