Next Article in Journal
Cell-Free Osteochondral Scaffold for the Treatment of Focal Articular Cartilage Defects in Early Knee OA: 5 Years’ Follow-Up Results
Next Article in Special Issue
Clinical and Ultrasound Thyroid Nodule Characteristics and Their Association with Cytological and Histopathological Outcomes: A Retrospective Multicenter Study in High-Resolution Thyroid Nodule Clinics
Previous Article in Journal
The Effects of Helmet Therapy Relative to the Size of the Anterior Fontanelle in Nonsynostotic Plagiocephaly: A Retrospective Study
Previous Article in Special Issue
Correlations between Molecular Landscape and Sonographic Image of Different Variants of Papillary Thyroid Carcinoma
Open AccessArticle

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

Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea
*
Author to whom correspondence should be addressed.
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)
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 artificial intelligence; thyroid nodule classification; deep learning; Fast Fourier transform; spatial domain; frequency domain
Show Figures

Figure 1

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop