Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence
Computer-aided diagnosis systems have been developed to assist doctors in diagnosing thyroid nodules to reduce errors made by traditional diagnosis methods, which are mainly based on the experiences of doctors. Therefore, the performance of such systems plays an important role in enhancing the quali...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/7/1822 |
| _version_ | 1798006648901468160 |
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| author | Dat Tien Nguyen Jin Kyu Kang Tuyen Danh Pham Ganbayar Batchuluun Kang Ryoung Park |
| author_facet | Dat Tien Nguyen Jin Kyu Kang Tuyen Danh Pham Ganbayar Batchuluun Kang Ryoung Park |
| author_sort | Dat Tien Nguyen |
| collection | DOAJ |
| description | Computer-aided diagnosis systems have been developed to assist doctors in diagnosing thyroid nodules to reduce errors made by traditional diagnosis methods, which are mainly based on the experiences of doctors. Therefore, the performance of such systems plays an important role in enhancing the quality of a diagnosing task. Although there have been the state-of-the art studies regarding this problem, which are based on handcrafted features, deep features, or the combination of the two, their performances are still limited. To overcome these problems, we propose an ultrasound image-based diagnosis of the malignant thyroid nodule method using artificial intelligence based on the analysis in both spatial and frequency domains. Additionally, we propose the use of weighted binary cross-entropy loss function for the training of deep convolutional neural networks to reduce the effects of unbalanced training samples of the target classes in the training data. Through our experiments with a popular open dataset, namely the thyroid digital image database (TDID), we confirm the superiority of our method compared to the state-of-the-art methods. |
| first_indexed | 2024-04-11T12:59:03Z |
| format | Article |
| id | doaj.art-1be08de72e764556974b4d333931b604 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:59:03Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1be08de72e764556974b4d333931b6042022-12-22T04:23:00ZengMDPI AGSensors1424-82202020-03-01207182210.3390/s20071822s20071822Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial IntelligenceDat Tien Nguyen0Jin Kyu Kang1Tuyen Danh Pham2Ganbayar Batchuluun3Kang Ryoung Park4Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, KoreaComputer-aided diagnosis systems have been developed to assist doctors in diagnosing thyroid nodules to reduce errors made by traditional diagnosis methods, which are mainly based on the experiences of doctors. Therefore, the performance of such systems plays an important role in enhancing the quality of a diagnosing task. Although there have been the state-of-the art studies regarding this problem, which are based on handcrafted features, deep features, or the combination of the two, their performances are still limited. To overcome these problems, we propose an ultrasound image-based diagnosis of the malignant thyroid nodule method using artificial intelligence based on the analysis in both spatial and frequency domains. Additionally, we propose the use of weighted binary cross-entropy loss function for the training of deep convolutional neural networks to reduce the effects of unbalanced training samples of the target classes in the training data. Through our experiments with a popular open dataset, namely the thyroid digital image database (TDID), we confirm the superiority of our method compared to the state-of-the-art methods.https://www.mdpi.com/1424-8220/20/7/1822ultrasound imagemalignant thyroid noduleartificial intelligencedeep learningweighted binary cross-entropy loss |
| spellingShingle | Dat Tien Nguyen Jin Kyu Kang Tuyen Danh Pham Ganbayar Batchuluun Kang Ryoung Park Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence Sensors ultrasound image malignant thyroid nodule artificial intelligence deep learning weighted binary cross-entropy loss |
| title | Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence |
| title_full | Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence |
| title_fullStr | Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence |
| title_full_unstemmed | Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence |
| title_short | Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence |
| title_sort | ultrasound image based diagnosis of malignant thyroid nodule using artificial intelligence |
| topic | ultrasound image malignant thyroid nodule artificial intelligence deep learning weighted binary cross-entropy loss |
| url | https://www.mdpi.com/1424-8220/20/7/1822 |
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