A Grey Wolf-Based Method for Mammographic Mass Classification
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: AI in cancer detection · Image Retrieval and Classification Techniques · Radiomics and Machine Learning in Medical Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Computer Science Department, Faculty of Computers and Informatics, Suez Canal University, Ismailia 41522, Egypt
Department of Information Technology, College of Computing and Information Technology at Khulais, University of Jeddah, Jeddah 21959, Saudi Arabia
Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia
School of Science, Engineering, and Environment, University of Salford, Salford M5 4WT, UK
Department of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Department of Mathematics, Faculty of Science, Sohag University, Sohag 82524, Egypt
References (35)
Show source-provided references
1. World Health Organization (WHO) (2020, August 01). Preventing Cancer. Available online: https://www.who.int/cancer/prevention/diagnosis-screening/breast-cancer/en/.
2. World Health Organization (WHO) (2020). WHO Report on Cancer: Setting Priorities, Investing Wisely and Providing Care for All, World Health Organization.
3. AlFayez, F., El-Soud, M.W.A., and Gaber, T. (2020). Thermogram Breast Cancer Detection: A comparative study of two machine learning techniques. Appl. Sci., 10.10.3390/app10020551 ↗
4. Ali, M.A., Sayed, G.I., Gaber, T., Hassanien, A.E., Snasel, V., and Silva, L.F. (2015, January 13–16). Detection of breast abnormalities of thermograms based on a new segmentation method. Proceedings of the 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), Lodz, Poland.10.15439/2015F318 ↗
5. Breast cancer diagnosis in digital mammogram using multiscale curvelet transform10.1016/j.compmedimag.2009.11.002 ↗
6. A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram10.1016/j.compbiomed.2010.02.002 ↗
7. A theory for multiresolution signal decomposition: The wavelet representation10.1109/34.192463 ↗
8. Contourlet-based mammography mass classification using the SVM family10.1016/j.compbiomed.2009.12.006 ↗
9. A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation10.1016/j.compbiomed.2011.10.016 ↗
10. Multi-scale textural feature extraction and particle swarm optimization based model selection for false positive reduction in mammography10.1016/j.compmedimag.2015.02.005 ↗
11. An Optimized Feature Selection Method for Breast Cancer Diagnosis in Digital Mammogram using Multiresolution Representation10.12785/amis/080629 ↗
12. Breast cancer diagnosis in digitized mammograms using curvelet moments10.1016/j.compbiomed.2015.06.012 ↗
13. Eltoukhy, M.M., Elhoseny, M., Hosny, K.M., and Singh, A.K. (2018). Computer aided detection of mammographic mass using exact Gaussian–Hermite moments. J. Ambient. Intell. Humaniz. Comput., 1–9.10.1007/s12652-018-0905-1 ↗
14. Fusion of Orthogonal Moment Features for Mammographic Mass Detection and Diagnosis10.1109/ACCESS.2020.3008038 ↗
15. Grey wolf optimization trained feed foreword neural network for breast cancer classification
16. A computer-aided diagnosis system using Tchebichef features and improved grey wolf optimized extreme learning machine10.1007/s10489-018-1294-z ↗
17. Vosooghifard, M., and Ebrahimpour, H. (2015, January 29). Applying Grey Wolf Optimizer-based decision tree classifer for cancer classification on gene expression data. Proceedings of the 2015 5th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran.10.1109/ICCKE.2015.7365818 ↗
18. Saabia, A.B.R., AbdEl-Hafeez, T., and Zaki, A.M. (2018). Face recognition based on Grey Wolf Optimization for feature selection. International Conference on Advanced Intelligent Systems and Informatics, Springer.10.1007/978-3-319-99010-1_25 ↗
19. Grey Wolf optimisation-based feature selection and classification for facial emotion recognition10.1049/iet-bmt.2017.0160 ↗
20. Texture extraction: An evaluation of ridgelet, wavelet and co-occurrence based methods applied to mammograms10.1016/j.eswa.2012.03.020 ↗
21. Mallat, S. (1999). A Wavelet Tour of Signal Processing, Academic Press. [2nd ed.].10.1016/B978-012466606-1/50008-8 ↗
22. Fast Discrete Curvelet Transforms10.1137/05064182X ↗
23. Candes, E., and Donoho, D. (2000). Curvelets, multiresolution representation, and scaling laws. Wavelet Applications in Signal and Image Processing VIII, Sampling and Approximation, SPIE, International Symposium on Optical Science and Technology.10.1117/12.408568 ↗
24. Koutroumbas, K., and Theodoridis, S. (2008). Pattern Recognition, Elsevier.
25. First and Second Order Statistics Features for Classification of Magnetic Resonance Brain Images
26. Chang, H.H., and Linh, N.V. (2017). Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems. Energies, 10.10.3390/en10050611 ↗
27. Grey Wolf Optimizer10.1016/j.advengsoft.2013.12.007 ↗
28. A New Competitive Binary Grey Wolf Optimizer to Solve the Feature Selection Problem in EMG Signals Classification10.3390/computers7040058 ↗
29. A random forest guided tour10.1007/s11749-016-0481-7 ↗
30. Heath, M., Bowyer, K., Kopans, D., Kegelmeyer, P., Moore, R., Chang, K., and Munishkumaran, S. (1998). Current status of the digital database for screening mammography. Digital Mammography, Springer.10.1007/978-94-011-5318-8_75 ↗
31. Modified grey wolf optimizer for global engineering optimization
32. Feature sub-set selection metrics for Arabic text classification10.1016/j.patrec.2011.07.010 ↗
33. Machine Learning in Automated Text Categorization10.1145/505282.505283 ↗
34. A feature selection technique for classificatory analysis10.1016/j.patrec.2004.08.015 ↗
35. El-Soud, M.W.A., Gaber, T., AlFayez, F., and Eltoukhy, M.M. (2020). Implicit authentication method for smartphone users based on rank aggregation and random forest. Alex. Eng. J.