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Classification of Ultrasound Thyroid Nodule Images by Computer-Aided Diagnosis: A Technical Review

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Computational Vision and Bio-Inspired Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1318))

Abstract

The thyroid is an indispensable gland of the human endocrine system that secretes hormones which have a significant effect on the metabolic rate and protein synthesis. The thyroid is susceptible to a variety of disorders. One among them is the formation of a thyroid nodule, an extraneous mass formed at the thyroid gland requiring medical attention and diagnosis. About 5–10 nodules out of 100 are malignant (cancerous). When a formation of nodules is discernible to the doctors, they call for a diagnostic blood test, often perfunctory, and do not differentiate malignant and benign tumours. This is where ultrasonography comes across as a better option. Automation of ultrasonography diagnosis results in a decrease in reporting time as well as provides a provisional diagnosis before the doctors’ expert opinion. Thus, deep learning methods were suggested and produced better results. Initially, region of interest (ROI) and feature extraction were done before applying machine learning models like support vector machines and multilayer perceptrons. Nevertheless, the feature selection required in machine learning methods was a long-drawn process, often involving the elements of trial and error. Deep convolutional neural networks along with histogram of gradients (HOG)-aided feature extraction which was used along classifiers have yielded high specificity and sensitivity values along with the accuracy. In this paper, we have studied and compared the efficacy of the application of various deep convolutional networks for the diagnosis of malignancy in thyroid nodules.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, 603203, India

    Siddhant Baldota & C. Malathy

Authors
  1. Siddhant Baldota
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  2. C. Malathy
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Correspondence to C. Malathy .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, RVS Technical Campus, Coimbatore, Tamil Nadu, India

    S. Smys

  2. Departamento de Engenharia Mecanica, SDI, Faculty of Engineering, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  3. Czech Technical University in Prague, Prague, Czech Republic

    Robert Bestak

  4. College of Graduate Studies, University of Central Florida, Orlando, FL, USA

    Fuqian Shi

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Baldota, S., Malathy, C. (2021). Classification of Ultrasound Thyroid Nodule Images by Computer-Aided Diagnosis: A Technical Review. In: Smys, S., Tavares, J.M.R.S., Bestak, R., Shi, F. (eds) Computational Vision and Bio-Inspired Computing. Advances in Intelligent Systems and Computing, vol 1318. Springer, Singapore. https://doi.org/10.1007/978-981-33-6862-0_30

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