Automated breast cancer detection and classification using ultrasound images: A survey

By Heng-Da Cheng, Juan Shan, Wen Ju, Yanhui Guo, Ling Zhang. Pattern Recognition, 2015

778 citations recorded by the source corpus.

Breast cancer is the second leading cause of death for women all over the world. Since the cause of the disease remains unknown, early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Ultrasound imaging is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast. In order to eliminate the operator dependency and improve the diagnostic accuracy, computer-aided diagnosis (CAD) system is a valuable and beneficial means for breast cancer detection and classification. Generally, a CAD system consists of four stages: preprocessing, segmentation, feature extraction and selection, and classification. In this paper, the approaches used in these stages are summarized and their advantages and disadvantages are discussed. The performance evaluation of CAD system is investigated as well.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/9499e0322d1e50dd65a5d8511ce253531685f0b2

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Citations in the corpus

Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks (2018, positive): “BREAST cancer is one of the leading causes of death for women worldwide and it is expected that more than 8% of women will develop breast cancer during their lifetime [1].”
Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. (2013, background): “The features of breast ultrasound images can be classified into four categories: texture, morphology, descriptor, and modelbased features [94].”
Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm (2014, methodology): “A key stage of classiﬁcation by CAD schemes is feature analysis and extraction (Cheng, Shan, Ju, Guo, & Zhang, 2010; Cherkassky & Mulier, 2007).”
Shadow Detection for Ultrasound Images Using Unlabeled Data and Synthetic Shadows (2019, background): “To alleviate this, there have been many attempts to support diagnosing by image recognition (Noble and Boukerroui, 2006; Cheng et al., 2010).”
Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey (2018, background): “There have been many studies which summarized the research of ultrasound CAD [10, 12, 13].”