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Breast Cancer Detection in Saudi Arabian Women Using Hybrid Machine Learning on Mammographic Images

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

1 Division of Radiology, Department of Medicine, Medical College, Najran University, Najran, 61441, Kingdom of Saudi Arabia
2 Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal, 57000, Pakistan
3 Department of Radiology, College of Medicine, Qassim University, Buraidah, 52571, Kingdom of Saudi Arabia
4 Department of Radiology, King Fahad Specialist Hospital, Buraydah, 52571, Kingdom of Saudi Arabia
5 Electrical Engineering Department, College of Engineering, Najran University, Najran, 61441, Kingdom of Saudi Arabia
6 Radiology Department, Human Medicine College, Zagazig University, Zagazig, 44631, Egypt
7 Department of Surgery, College of Medicine, Najran University, Najran, 61441, Kingdom of Saudi Arabia
8 Department of Computer Science, Bahauddin Zakariya University, Multan, 66000, Pakistan
9 Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran, 61441, Kingdom of Saudi Arabia

* Corresponding Author: Khalaf Alshamrani. Email: email

Computers, Materials & Continua 2022, 72(3), 4833-4851.


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 was collected from different hospitals in the Qassim health cluster of Saudi Arabia. The collection of all categories of BI-RADs is one of the major contributions of this paper. Another significant contribution is the development of a hybrid approach through the integration of CNN and SVM. The proposed hybrid approach uses three CNN models to obtain ensemble CNN model results. This ensemble model saves the values to integrate them with SVM. The proposed system achieved a classification accuracy, sensitivity, specificity, precision, and F1-score of 93.6%, 94.8%, 96.9%, 96.6%, and 95.7%, respectively. The proposed model achieved better performance compared to previously available methods.


Cite This Article

Y. Edrees Almalki1, A. Shaf, T. Ali, M. Aamir, S. Khalid Alduraibi et al., "Breast cancer detection in saudi arabian women using hybrid machine learning on mammographic images," Computers, Materials & Continua, vol. 72, no.3, pp. 4833–4851, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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