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Ryeonhui Kim, Kyuseok Kim and Youngjin Lee
Ultrasound imaging is widely used as a noninvasive lesion detection method in diagnostic medicine. Improving the quality of these ultrasound images is very important for accurate diagnosis, and deep learning-based algorithms have gained significant atten...
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N. Aidossov, Vasilios Zarikas, Aigerim Mashekova, Yong Zhao, Eddie Yin Kwee Ng, Anna Midlenko and Olzhas Mukhmetov
Breast cancer comprises a serious public health concern. The three primary techniques for detecting breast cancer are ultrasound, mammography, and magnetic resonance imaging (MRI). However, the existing methods of diagnosis are not practical for regular ...
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Alireza Rezazadeh, Yasamin Jafarian and Ali Kord
Image classification is widely used to build predictive models for breast cancer diagnosis. Most existing approaches overwhelmingly rely on deep convolutional networks to build such diagnosis pipelines. These model architectures, although remarkable in p...
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Shahriar Mahmud Kabir, Mohammed I. H. Bhuiyan, Md Sayed Tanveer and ASM Shihavuddin
This study presents two new approaches based on Weighted Contourlet Parametric (WCP) images for the classification of breast tumors from B-mode ultrasound images. The Rician Inverse Gaussian (RiIG) distribution is considered for modeling the statistics o...
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Yi-Wei Chang, Yun-Ru Chen, Chien-Chuan Ko, Wei-Yang Lin and Keng-Pei Lin
The breast ultrasound is not only one of major devices for breast tissue imaging, but also one of important methods in breast tumor screening. It is non-radiative, non-invasive, harmless, simple, and low cost screening. The American College of Radiology ...
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Yuliana Jiménez-Gaona, María José Rodríguez-Álvarez and Vasudevan Lakshminarayanan
This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make us...
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