Abstract
Nowadays, computer-aided diagnosis (CAD) systems play an important role in supporting doctors and improving the performance of thyroid nodule detection which able to reduce the physician’s examination burden. In addition, medical data have certain characteristics that make their application very challenging on the existing system. In this paper, we propose an ultrasound image-based of thyroid nodule detection method using state-of-the-art object detectors. We employed radiologist knowledge from clinical studies such as size and aspect ratio distributions of real nodules to generate better region proposals and improve the detection accuracy. Additionally, we contribute a new Vietnamese thyroid nodule dataset, which is collected from Vietnamese Hospitals and validated by experienced radiologists. With this dataset, we expect to provide an essential medical imaging research resource for CAD development and validation. Through experimental results, our detection network FasterIncResnet** that was optimized with radiologist’s knowledge yielded the highest performance compared with original-based methods. Our optimized detection sensitivity, specificity, and AUC were 0.87, 0.86, and 0.89, respectively, on the Vietnamese dataset. The overall performance improves ~2.4% compared with the original FasterIncResnet method. The result proved the effectiveness of the proposed method on a new Vietnamese dataset in order to build an accurate system for thyroid nodule detection.
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Pham, TC. et al. (2021). Evaluating the Deep Convolutional Neural Network for Thyroid Nodule Detection on Vietnamese Ultrasound Dataset. In: Pan, JS., Li, J., Ryu, K.H., Meng, Z., Klasnja-Milicevic, A. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 212. Springer, Singapore. https://doi.org/10.1007/978-981-33-6757-9_45
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DOI: https://doi.org/10.1007/978-981-33-6757-9_45
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