Yassir Edrees Almalki11, Ahmad Shaf2, Tariq Ali2, Muhammad Aamir2, Sharifa Khalid Alduraibi3, Shoayea Mohessen Almutiri4, Muhammad Irfan5, Mohammad Abd Alkhalik Basha6, Alaa Khalid Alduraibi3, Abdulrahman Manaa Alamri7, Muhammad Zeeshan Azam8, Khalaf Alshamrani9,*, Hassan A. Alshamrani9
CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4833-4851, 2022, DOI:10.32604/cmc.2022.027111
Abstract Breast cancer (BC) is the most common cause of women’s deaths worldwide. The mammography technique is the most important modality for the detection of BC. To detect abnormalities in mammographic images, the Breast Imaging Reporting and Data System (BI-RADs) is used as a baseline. The correct allocation of BI-RADs categories for mammographic images is always an interesting task, even for specialists. In this work, to detect and classify the mammogram images in BI-RADs, a novel hybrid model is presented using a convolutional neural network (CNN) with the integration of a support vector machine (SVM). The dataset used in this research… More >
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