Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains

Nguyen, Dat Tien; Pham, Tuyen Danh; Batchuluun, Ganbayar; Yoon, Hyo Sik; Park, Kang Ryoung

doi:10.3390/jcm8111976

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Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains

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Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea

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J. Clin. Med. 2019, 8(11), 1976; https://doi.org/10.3390/jcm8111976

Received: 24 October 2019 / Revised: 8 November 2019 / Accepted: 10 November 2019 / Published: 14 November 2019

(This article belongs to the Special Issue Imaging and Imaging-Based Management of Thyroid Nodules)

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Abstract

Image-based computer-aided diagnosis (CAD) systems have been developed to assist doctors in the diagnosis of thyroid cancer using ultrasound thyroid images. However, the performance of these systems is strongly dependent on the selection of detection and classification methods. Although there are previous researches on this topic, there is still room for enhancement of the classification accuracy of the existing methods. To address this issue, we propose an artificial intelligence-based method for enhancing the performance of the thyroid nodule classification system. Thus, we extract image features from ultrasound thyroid images in two domains: spatial domain based on deep learning, and frequency domain based on Fast Fourier transform (FFT). Using the extracted features, we perform a cascade classifier scheme for classifying the input thyroid images into either benign (negative) or malign (positive) cases. Through expensive experiments using a public dataset, the thyroid digital image database (TDID) dataset, we show that our proposed method outperforms the state-of-the-art methods and produces up-to-date classification results for the thyroid nodule classification problem. View Full-Text

Keywords: artificial intelligence; thyroid nodule classification; deep learning; Fast Fourier transform; spatial domain; frequency domain

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MDPI and ACS Style

Nguyen, D.T.; Pham, T.D.; Batchuluun, G.; Yoon, H.S.; Park, K.R. Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains. J. Clin. Med. 2019, 8, 1976. https://doi.org/10.3390/jcm8111976

AMA Style

Nguyen DT, Pham TD, Batchuluun G, Yoon HS, Park KR. Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains. Journal of Clinical Medicine. 2019; 8(11):1976. https://doi.org/10.3390/jcm8111976

Chicago/Turabian Style

Nguyen, Dat T., Tuyen D. Pham, Ganbayar Batchuluun, Hyo S. Yoon, and Kang R. Park. 2019. "Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains" Journal of Clinical Medicine 8, no. 11: 1976. https://doi.org/10.3390/jcm8111976

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Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains

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