Thyroid Nodule Segmentation in Ultrasound Image Based on Information Fusion of Suggestion and Enhancement Networks

Dat Tien Nguyen, Jiho Choi and Kang Ryoung Park ()
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Dat Tien Nguyen: Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Korea
Jiho Choi: Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Korea
Kang Ryoung Park: Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Korea

Mathematics, 2022, vol. 10, issue 19, 1-20

Abstract: Computer-aided diagnosis/detection (CADx) systems have been used to help doctors in improving the quality of diagnosis and treatment processes in many serious diseases such as breast cancer, brain stroke, lung cancer, and bone fracture. However, the performance of such systems has not been completely accurate. The key factor in CADx systems is to localize positive disease lesions from the captured medical images. This step is important as it is used not only to localize lesions but also to reduce the effect of noise and normal regions on the overall CADx system. In this research, we proposed a method to enhance the segmentation performance of thyroid nodules in ultrasound images based on information fusion of suggestion and enhancement segmentation networks. Experimental results with two open databases of thyroid digital image databases and 3DThyroid databases showed that our method resulted in a higher performance compared to current up-to-date methods.

Keywords: medical image processing; thyroid nodule segmentation; artificial intelligence; attention network; suggestion and enhancement networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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