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A Study on Thyroid Nodule Image Classification System Using Small Amount of Training Samples

Hoan Quoc Tuan, Pham Xuan Hien, Trinh Xuan Thang

Annals of Computer Science and Information Systems

2022

Vol. 33

129--133

To reduce errors caused by traditional diagnostic methods that rely heavily on physician experience, the diagnostic systems based on computer-aided have been researched and developed to assist physicians in diagnosing thyroid disease. Therefore, performance of the computer systems plays an important role to improve the quality of diagnostic processes. Although there has been a number of publish related to this issue, those studies still have limitations in which needing large data sets for training classification models is considered the most concerning limitation of previous studies. To solve this limitations, in this work, a classification method using artificial intelligence with a small amount of data to analyze thyroid ultrasound images was proposed. Thus we can save time and effort for data collection and the classification model processing time. Through baseline tests with an open data set, especially thyroid digital image database (TDID), the proposed method has improved the limitations of previous methods.