RESEARCH ARTICLE
Breast cancer: A hybrid method for feature selection and classification in digital mammography
Shankar Thawkar,
Vijay Katta,
Ajay Raj Parashar,
Law Kumar Singh,
Munish Khanna,
Corresponding Author
Shankar Thawkar
Department of Information Technology, Hindustan College of Science and Technology, Mathura, Uttar Pradesh, India
Correspondence
Shankar Thawkar, Department of Information Technology, Hindustan College of Science and Technology, Mathura, Uttar Pradesh, India.
Email: [email protected]
Search for more papers by this authorVijay Katta
Department of Computer Science and Engineering, Hindustan College of Science and Technology, Mathura, Uttar Pradesh, India
Search for more papers by this authorAjay Raj Parashar
Department of Information Technology, Hindustan College of Science and Technology, Mathura, Uttar Pradesh, India
Search for more papers by this authorLaw Kumar Singh
Department of Computer Engineering and Applications, G. L. A. University, Mathura, Uttar Pradesh, India
Search for more papers by this authorMunish Khanna
Department of Computer Science and Engineering, Hindustan College of Science and Technology, Mathura, Uttar Pradesh, India
Search for more papers by this authorShankar Thawkar,
Vijay Katta,
Ajay Raj Parashar,
Law Kumar Singh,
Munish Khanna,
Corresponding Author
Shankar Thawkar
Department of Information Technology, Hindustan College of Science and Technology, Mathura, Uttar Pradesh, India
Correspondence
Shankar Thawkar, Department of Information Technology, Hindustan College of Science and Technology, Mathura, Uttar Pradesh, India.
Email: [email protected]
Search for more papers by this authorVijay Katta
Department of Computer Science and Engineering, Hindustan College of Science and Technology, Mathura, Uttar Pradesh, India
Search for more papers by this authorAjay Raj Parashar
Department of Information Technology, Hindustan College of Science and Technology, Mathura, Uttar Pradesh, India
Search for more papers by this authorLaw Kumar Singh
Department of Computer Engineering and Applications, G. L. A. University, Mathura, Uttar Pradesh, India
Search for more papers by this authorMunish Khanna
Department of Computer Science and Engineering, Hindustan College of Science and Technology, Mathura, Uttar Pradesh, India
Search for more papers by this authorAbstract
In this article, a hybrid approach based on the Whale optimization algorithm (WOA) and the Dragonfly algorithm (DA) is proposed for breast cancer diagnosis. The hybrid WOADA method selects features based on the fitness value. These features are used to predict the breast masses as benign or malignant using artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) as classifiers. The proposed solution is evaluated by using 651 mammograms. The results demonstrate that the WOADA technique outperforms the basic WOA and DA approaches. The accuracy of the suggested WOADA algorithm is 97.84%, with a Kappa value of 0.9477 and an AUC value of 0.972 ± 0.007 for the ANN classifier. Likewise, the ANFIS classifier achieved 98.00% accuracy with a Kappa value of 0.96 and an AUC value of 0.998 ± 0.001. In addition, the viability of the hybrid WOADA technique was evaluated on four benchmark datasets and then compared with four state-of-the-art algorithms and published approaches.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflicts of interest.
Open Research
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are openly available in Digital Database for Screening Mammography at http://www.eng.usf.edu/cvprg/mammography/database.html.
REFERENCES
- 1Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021; 71(3): 209-249. doi:10.3322/caac.21660
- 2Løberg M, Lousdal ML, Bretthauer M, Kalager M. Benefits and harms of mammography screening. Breast Cancer Res. 2015; 17: 63.
- 3Rangayyan RM, Ayres FJ, Leo Desautels JE. A review of computer-aided diagnosis of breast cancer: toward the detection of subtle signs. J Franklin Inst. 2007; 344: 312-348.
- 4Bleyer A, Welch HG. Effect of three decades of screening mammography on breast-cancer incidence. N Engl J Med. 2012; 367: 1998-2005.
- 5Fanizzi A, Basile TMA, Losurdo L, et al. Hough transform for clustered microcalcifications detection in full-field digital mammograms. Applications of Digital Image Processing XL. Vol 10396. International Society for Optics and Photonics; 2017: 1039616.
- 6Jalalian A, Mashohor SB, Mahmud HR, Saripan MIB, Ramli ARB, Karasfi B. Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clin Imaging. 2013; 37(3): 420-426.
- 7Bellotti R, Bagnasco S, Bottigli U, et al. The MAGIC-5 project: medical applications on a GRID infrastructure connection. IEEE Symposium Conference Record Nuclear Science 2004. Vol 3. IEEE; 2004: 1902-1906.
10.1109/NSSMIC.2004.1462616 Google Scholar
- 8Avanzo M, Porzio M, Lorenzon L, et al. Artificial intelligence applications in medical imaging: a review of the medical physics research in Italy. Phys Med. 2021; 83: 221-241.
- 9Ganesan K, Acharya UR, Chua CK, Min LC, Abraham KT, Ng KH. Computer-aided breast cancer detection using mammograms: a review. IEEE Revi Biomed Eng. 2012; 6: 77-98. doi:10.1109/RBME.2012.2232289
- 10Liu H, Motoda H. Feature selection for knowledge discovery and data mining. Springer Science & Business Media; 2012. doi:10.1007/978-1-4615-5689-3
10.1007/978?1?4615?5689?3 Google Scholar
- 11Liu H, Yu L. Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Know Data Eng. 2005; 17(4): 491-502.
- 12Li Y, Li T, Liu H. Recent advances in feature selection and its applications. Knowled Inform Syst. 2017; 53(3): 551-577.
- 13Yu L, Liu H. Feature selection for high-dimensional data: a fast correlation-based filter solution. Proceedings of the 20th International Conference on Machine Learning (ICML-03). AAAI Press; 2003: 856-863.
- 14Mirjalili S, Lewis A. The whale optimization algorithm. Adv Eng Softw. 2016; 95: 51-67. doi:10.1016/j.advengsoft.2016.01.008
- 15Mafarja M, Mirjalili S. Whale optimization approaches for wrapper feature selection. Appl Soft Comput. 2018; 62: 441-453. doi:10.1016/j.asoc.2017.11.006
- 16Agrawal RK, Kaur B, Sharma S. Quantum based whale optimization algorithm for wrapper feature selection. Appl Soft Comput. 2020; 89:106092. doi:10.1016/j.asoc.2020
- 17Moorthy U, Gandhi UD. A novel optimal feature selection technique for medical data classification using ANOVA based whale optimization. J Ambient Intell Human Comput. 2021; 12(3): 3527-3538. doi:10.1007/s12652-020-02592-w
- 18Hussien AG, Hassanien AE, Houssein EH, Amin M, Azar AT. New binary whale optimization algorithm for discrete optimization problems. Eng Optimiz. 2020; 52(6): 945-959. doi:10.1080/0305215X.2019.1624740
- 19Mirjalili S. Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl. 2016; 27(4): 1053-1073.
- 20Mafarja M, Heidari AA, Faris H, Mirjalili S, Aljarah I. Dragonfly algorithm: theory, literature review, and application in feature selection. Nature-Insp Optimiz. 2020; 811:47-67. doi:10.1007/978-3-030-12127-3_4
10.1007/978?3?030?12127?3_4 Google Scholar
- 21Mafarja MM, Eleyan D, Jaber I, Hammouri A, Mirjalili S. Binary dragonfly algorithm for feature selection. In 2017 International Conference on New Trends in Computing Sciences (ICTCS). IEEE; 2017: 12-17. doi:10.1109/ICTCS.2017.43
10.1109/ICTCS.2017.43 Google Scholar
- 22Sree Ranjini KS, Murugan S. Memory based hybrid dragonfly algorithm for numerical optimization problems. Exp Syst Appl. 2017; 83: 63-78. doi:10.1016/j.eswa.2017.04.033
- 23Sayed GI, Tharwat A, Hassanien AE. Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection. Appl Intell. 2019; 49(1): 188-205. doi:10.1007/s10489-018-1261-8
- 24Hammouri AI, Mafarja M, Al-Betar MA, Awadallah MA, Abu-Doush I. An improved dragonfly algorithm for feature selection. Knowl Based Syst. 2020; 203:106131. doi:10.1016/j.knosys.2020.106131
- 25Too J, Mirjalili S. A hyper learning binary dragonfly algorithm for feature selection: a COVID-19 case study. Knowl-Based Syst. 2021; 212:106553. doi:10.1016/j.knosys.2020.106553
- 26Mafarja MM, Mirjalili S. Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing. 2017; 260: 302-312. doi:10.1016/j.neucom.2017.04.053
- 27Abd El Aziz M, Ewees AA, Hassanien AE. Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation. Exp Syst Appl. 2017; 83: 242-256. doi:10.1016/j.eswa.2017.04.023
- 28Liu M, Yao X, Li Y. Hybrid whale optimization algorithm enhanced with Lévy flight and differential evolution for job shop scheduling problems. Appl Soft Comput. 2020; 87:105954. doi:10.1016/j.asoc.2019.105954
- 29Mohammadzadeh H, Gharehchopogh FS. A novel hybrid whale optimization algorithm with flower pollination algorithm for feature selection: case study email spam detection. Comput Intell. 2021; 37(1): 176-209. doi:10.1111/coin.12397
- 30Cui X, Li Y, Fan J, Wang T, Zheng Y. A hybrid improved dragonfly algorithm for feature selection. IEEE Access. 2020; 8: 155619-155629. doi:10.1109/ACCESS.2020.3012838
- 31Thawkar S. A hybrid model using teaching–learning-based optimization and Salp swarm algorithm for feature selection and classification in digital mammography. J Ambi Intell Humaniz Comput. 2021; 12(9): 8793-8808. doi:10.1007/s12652-020-02662-z
- 32Thawkar S, Sharma S, Khanna M, Kumar Singh L. Breast cancer prediction using a hybrid method based on butterfly optimization algorithm and ant lion optimizer. Comput Biol Med. 2021; 139:104968.
- 33Thawkar S. Feature selection and classification in mammography using hybrid crow search algorithm with Harris hawks optimization. Biocybernet Biomed Eng. 2022; 42(4): 1094-1111.
- 34Fang H, Fan H, Lin S, Qing Z, Sheykhahmad FR. Automatic breast cancer detection based on optimized neural network using whale optimization algorithm. Int J Imag Syst Technol. 2021; 31(1): 425-438.
- 35Turabieh H, Muhanna M. GA-based feature selection with ANFIS approach to breast cancer recurrence. Int J Comput Sci Issues. 2016; 13(1): 36-41.
10.20943/IJCSI-201602-3641 Google Scholar
- 36Wolpert DH, Macready WG. No free lunch theorems for optimization. IEEE Trans Evolution Comput. 1997; 1(1): 67-82.
10.1109/4235.585893 Google Scholar
- 37Thawkar S, Ingolikar R. Classification of masses in digital mammograms using biogeography-based optimization technique. J King Saud Univ Comput Inform Sci. 2020; 32(10): 1140-1148. doi:10.1016/j.jksuci.2018.01.004
- 38Sameti M, Ward RK, Morgan-Parkes J, Palcic B. A method for detection of malignant masses in digitized mammograms using a fuzzy segmentation algorithm. Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. ‘Magnificent Milestones and Emerging Opportunities in Medical Engineering’ (Cat. No. 97CH36136). Vol 2. IEEE; 1997: 513-516. doi:10.1109/IEMBS.1997.757658
10.1109/IEMBS.1997.757658 Google Scholar
- 39Li H, Wang Y, Liu KR, Lo SC, Freedman MT. Computerized radiographic mass detection. II. Decision support by featured database visualization and modular neural networks. IEEE Trans Med Imaging. 2001; 20(4): 302-313. doi:10.1109/42.921479
- 40Reynolds CW. Flocks, herds and schools: a distributed behavioral model. Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques. Association for Computing Machinery; 1987: 25-34. doi:10.1145/37401.37406
- 41Cheng HD, Shi XJ, Min R, Hu LM, Cai XP, Du HN. Approaches for automated detection and classification of masses in mammograms. Pattern Recog. 2006; 39(4): 646-668. doi:10.1016/j.patcog.2005.07.006
- 42Saritas I, Ozkan IA, Sert IU. Prognosis of prostate cancer by artificial neural networks. Exp Syst Appl. 2010; 37(9): 6646-6650. doi:10.1016/j.eswa.2010.03.056
- 43Jang JS. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern. 1993; 23(3): 665-685.
- 44Bowyer K, Kopans D, Kegelmeyer WP, et al. The digital database for screening mammography. In Third International Workshop on Digital Mammography 58:27. 1996.
- 45Heath M, Bowyer K, Kopans D, et al. Current status of the digital database for screening mammography. Digital Mammography. Springer; 1998: 457-460. doi:10.1007/978-94-011-5318-8_75
10.1007/978-94-011-5318-8_75 Google Scholar
- 46Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977; 33(1):159-174.
- 47Swets JA. Measuring the accuracy of diagnostic systems. Science. 1988; 240(4857): 1285-1293. doi:10.1126/science.3287615
- 48Huang ML, Hung YH, Lee WM, Li RK, Wang TH. Usage of case-based reasoning, neural network and adaptive neuro-fuzzy inference system classification techniques in breast cancer dataset classification diagnosis. J Med Syst. 2012; 36(2): 407-414.
- 49Liu X, Tang J. Mass classification in mammograms using selected geometry and texture features, and a new SVM-based feature selection method. IEEE Syst J. 2013; 8(3): 910-920.
- 50Khan S, Hussain M, Aboalsamh H, Mathkour H, Bebis G, Zakariah M. Optimized Gabor features for mass classification in mammography. Appl Soft Comput. 2016; 44: 267-280.
- 51de Lima SM, da Silva-Filho AG, Dos Santos WP. Detection and classification of masses in mammographic images in a multi-kernel approach. Comput Methods Programs Biomed. 2016; 134: 11-29.
- 52Chokri F, Farida MH. Mammographic mass classification according to Bi-RADS lexicon. IET Comput Vis. 2017; 11(3): 189-198.
- 53Dora L, Agrawal S, Panda R, Abraham A. Optimal breast cancer classification using Gauss–Newton representation based algorithm. Exp Syst Appl. 2017; 85: 134-145.
- 54Celaya-Padilla JM, Guzmán-Valdivia CH, Galván-Tejada CE, et al. Contralateral asymmetry for breast cancer detection: a CADx approach. Biocybern Biomed Eng. 2018; 38(1): 115-125.
- 55Dalwinder S, Birmohan S, Manpreet K. Simultaneous feature weighting and parameter determination of neural networks using ant lion optimization for the classification of breast cancer. Biocybern Biomed Eng. 2020; 40(1): 337-351.
- 56Lbachir IA, Daoudi I, Tallal S. Automatic computer-aided diagnosis system for mass detection and classification in mammography. Multimed Tools Appl. 2021; 80(6): 9493-9525.
- 57Chakravarthy SS, Rajaguru H. Automatic detection and classification of mammograms using improved extreme learning machine with deep learning. IRBM. 2022; 43(1): 49-61.
