A grey wolf-based method for mammographic mass classification

Tahoun, M; Almazroi, AA; Alqarni, MA; Gaber, T; Mahmoud, EE; Eltoukhy, MM

Abstract

Breast cancer is one of the most prevalent cancer types with a high mortality rate in women worldwide. This devastating cancer still represents a worldwide public health concern in terms of high morbidity and mortality rates. The diagnosis of breast abnormalities is challenging due to different types of tissues and textural variations in intensity. Hence, developing an accurate computer-aided system (CAD) is very important to distinguish normal from abnormal tissues and define the abnormal tissues as benign or malignant. The present study aims to enhance the accuracy of CAD systems and to reduce its computational complexity. This paper proposes a method for extracting a set of statistical features based on curvelet and wavelet sub-bands. Then the binary grey wolf optimizer (BGWO) is used as a feature selection technique aiming to choose the best set of features giving high performance. Using public dataset, Digital Database for Screening Mammography (DDSM), different experiments have been performed with and without using the BGWO algorithm. The random forest classifier with 10-fold cross-validation is used to achieve the classification task to evaluate the selected set of features’ capability. The obtained results showed that when the BGWO algorithm is used as a feature selection technique, only 30.7% of the total features can be used to detect whether a mammogram image is normal or abnormal with ROC area reaching 1.0 when the fusion of both curvelet and wavelet features were used. In addition, in case of diagnosing the mammogram images as benign or malignant, the results showed that using BGWO algorithm as a feature selection technique, only 38.5% of the total features can be used to do so with high ROC area result at 0.871.

Citation

Tahoun, M., Almazroi, A., Alqarni, M., Gaber, T., Mahmoud, E., & Eltoukhy, M. (2020). A grey wolf-based method for mammographic mass classification. Applied Sciences, 10(23), e8422. https://doi.org/10.3390/app10238422

• Journal Article Type: Article
• Acceptance Date: Nov 21, 2020
• Publication Date: Nov 26, 2020
• Deposit Date: Nov 30, 2020
• Publicly Available Date: Nov 30, 2020
• Journal: Applied Sciences
• Publisher: MDPI
• Volume: 10
• Issue: 23
• Pages: e8422
• DOI: https://doi.org/10.3390/app10238422
• Publisher URL: https://doi.org/10.3390/app10238422
• Related Public URLs: http://www.mdpi.com/journal/applsci
• Additional Information: Additional Information : ** From MDPI via Jisc Publications Router ** Licence for this article: https://creativecommons.org/licenses/by/4.0/ **Journal IDs: eissn 2076-3417 **History: published 26-11-2020; accepted 21-11-2020