Misr Institute for Science and Technology, Cairo, Egypt.
Research Article
World Journal of Advanced Research and Reviews, 2021, 12(01), 308-314
Article DOI: 10.30574/wjarr.2021.12.1.0522
Received on 09 September 2021; revised on 15 October 2021; accepted on 17 October 2021
Breast cancer has become one of the most cancers among women in worldwide countries, as well as a leading cause of death. The use of ultrasound images in medical diagnosis and treatment of patients is critical. The success of cancer treatment and outcome is largely dependent on early detection. Imaging modalities such as ultrasonography are used to identify cancer. Ultrasound imaging is noninvasive, widely available, simple to use, and less expensive than other imaging technologies. As a result, ultrasonography is becoming more used as a cancer detection tool. Ultrasound imaging, on the other hand, is prone to noise and speckle artifacts. First, the ultrasound machine's raw picture data extraction is disabled. As a result, the process of recognizing malignant spots is prioritized. Artificial neural networks and other tissue characterization approaches are used. This technique was chosen because categorization and detection systems have greatly increased in their ability to assist medical experts in diagnosis. Manually classifying ultrasound images not only takes a long time and effort. As a result, a neural network classification-based automatic tissue characterization technique is proposed. Finally, the newly developed algorithms can aid specialists in recognizing suspicious aberrant tissue locations.
Breast Cancer; Ultrasound Imaging; Neural Networks; K-means
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Zeyad O Moussa and AE El Latif. Breast cancer detection in ultrasound imaging. World Journal of Advanced Research and Reviews, 2021, 12(1), 308-314. Article DOI: https://doi.org/10.30574/wjarr.2021.12.1.0522