Feature selection and classification in mammography using hybrid crow search algorithm with Harris hawks optimization

• Autorzy: Thawkar Shankar

Identyfikatory

DOI

10.1016/j.bbe.2022.09.001

• Języki publikacji: EN

Abstrakty

EN

The purpose of this study is to develop a hybrid algorithm for feature selection and classification of masses in digital mammograms based on the Crow search algorithm (CSA) and Harris hawks optimization (HHO). The proposed CSAHHO algorithm finds the best features depending on their fitness value, which is determined by an artificial neural network. Using an artificial neural network and support vector machine classifiers, the best features determined by CSAHHO are utilized to classify masses in mammograms as benign or malignant. The performance of the suggested method is assessed using 651 mammograms. Experimental findings show that the proposed CSAHHO tends to be the best as compared to the original CSA and HHO algorithms when evaluated using ANN. It achieves an accuracy of 97.85% with a kappa value of 0.9569 and area under curve A_Z = 0.982 ± 0.006. Furthermore, benchmark datasets are used to test the feasibility of the suggested approach and then compared with four state-of-the-art algorithms. The findings indicate that CSAHHO achieves high performance with the least amount of features and support to enhance breast cancer diagnosis.

Słowa kluczowe

EN

crow search algorithm; Harris hawks optimization; feature selection; artificial neural network; support vector machine; breast cancer

PL

selekcja cech; sztuczna sieć neuronowa; maszyna wektorów wsparcia; nowotwór piersi

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 4
• Strony: 1094--1111
• Opis fizyczny: Bibliogr. 99 poz., rys., tab., wykr.

Twórcy

autor

Thawkar Shankar

• Department of Information Technology, Hindustan College of Science and Technology, Mathura, Uttach Pradesh, India

Bibliografia

• [1] Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, 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):209–49 https://doi.org/10.3322/caac.21660.
• [2] Celaya-Padilla JM, Guzmán-Valdivia CH, Galván-Tejada CE, Galván-Tejada JI, Gamboa-Rosales H, Garza-Veloz I, et al. Contralateral asymmetry for breast cancer detection: a CADx approach. Biocybernet Biomed Eng 2018;38(1):115–25.
• [3] Fanizzi A, Basile TMA, Losurdo L, Amoroso N, Bellotti R, Bottigli U, et al. Hough transform for clustered microcalcifications detection in full-field digital mammograms ((2017, September, Vol. 10396, p. 1039616). International Society for Optics and Photonics).
• [4] Jalalian 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):420–6.
• [5] Bellotti, R., Bagnasco, S., Bottigli, U., Castellano, M., Cataldo, R., Catanzariti, E., ... & Zanon, E. (2004, October). The MAGIC-5 Project: medical applications on a GRID infrastructure connection. In IEEE Symposium Conference Record Nuclear Science 2004. (Vol. 3, pp. 1902-1906). IEEE.
• [6] Sapate S, Talbar S, Mahajan A, Sable N, Desai S, Thakur M. Breast cancer diagnosis using abnormalities on ipsilateral views of digital mammograms. Biocybernet Biomed Eng 2020;40(1):290–305.
• [7] Pawar, M.M., Talbar, S.N., & Dudhane, A. (2018). Local binary patterns descriptor based on sparse curvelet coefficients for false-positive reduction in mammograms. J Healthcare Eng, 2018.
• [8] Mori M, Akashi-Tanaka S, Suzuki S, Daniels MI, Watanabe C, Hirose M, et al. Diagnostic accuracy of contrast-enhanced spectral mammography in comparison to conventional full-field digital mammography in a population of women with dense breasts. Breast Cancer 2017;24(1):104–10.
• [9] Massafra R, Bove S, Lorusso V, Biafora A, Comes MC, Didonna V, et al. Radiomic feature reduction approach to predict breast cancer by contrast-enhanced spectral mammography images. Diagnostics 2021;11(4):684.
• [10] Patel, B. K., Lobbes, M. B. I., & Lewin, J. (2018, February). Contrast enhanced spectral mammography: a review. In Seminars in Ultrasound, CT and MRI (Vol. 39, No. 1, pp. 70-79). WB Saunders.
• [11] Lobbes MBI, Smidt ML, Houwers J, Tjan-Heijnen VC, Wildberger JE. Contrast enhanced mammography: techniques, current results, and potential indications. Clin Radiol 2013;68(9):935–44.
• [12] Goudarzi M, Maghooli K. Extraction of fuzzy rules at different concept levels related to image features of mammography for diagnosis of breast cancer. Biocybernet Biomed Eng 2018;38(4):1004–14.
• [13] Avanzo M, Porzio M, Lorenzon L, Milan L, Sghedoni R, Russo G, et al. Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy. Physica Medica 2021;83:221–41.
• [14] Abu-Amara F, Abdel-Qader I. Hybrid mammogram classification using rough set and fuzzy classifier. Int J Biomed Imaging 2009. https://doi.org/10.1155/2009/680508.
• [15] Ganesan K, Acharya UR, Chua CK, Min LC, Abraham KT, Ng KH. Computer-aided breast cancer detection using mammograms: a review. IEEE Rev Biomed Eng 2012;6:77–98.
• [16] L. Yu H. Liu Feature selection for high-dimensional data: A fast correlation-based filter solution In Proceedings of the 20th international conference on machine learning 2003 (ICML-03) 856–863.
• [17] Liu H, Motoda H. Feature selection for knowledge discovery and data mining. Springer Science & Business Media 2012. https://doi.org/10.1007/978-1-4615-5689-3.
• [18] Liu H, Yu L. Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng 2005;17(4):491–502.
• [19] Li Y, Li T, Liu H. Recent advances in feature selection and its applications. Knowl Inf Syst 2017;53(3):551–77.
• [20] Askarzadeh A. A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 2016;169:1–12.
• [21] Gupta D, Sundaram S, Khanna A, Hassanien AE, De Albuquerque VHC. Improved diagnosis of Parkinson’s disease using optimized crow search algorithm. Comput Electr Eng 2018;68:412–24.
• [22] Gupta D, Rodrigues JJ, Sundaram S, Khanna A, Korotaev V, de Albuquerque VH. Usability feature extraction using modified crow search algorithm: a novel approach. Neural Comput Appl 2018;1–11. https://doi.org/10.1007/s00521-018-3688-6.
• [23] Mohammadi F, Abdi H. A modified crow search algorithm (MCSA) for solving economic load dispatch problem. Appl Soft Comput 2018;71:51–65.
• [24] Oliva D, Hinojosa S, Cuevas E, Pajares G, Avalos O, Gálvez J. Cross entropy based thresholding for magnetic resonance brain images using Crow Search Algorithm. Expert Syst Appl 2017;79:164–80.
• [25] Rizk-Allah RM, Hassanien AE, Bhattacharyya S. Chaotic crow search algorithm for fractional optimization problems. Appl Soft Comput 2018;71:1161–75.
• [26] Sayed GI, Hassanien AE, Azar AT. Feature selection via a novel chaotic crow search algorithm. Neural Comput Appl 2019;31 (1):171–88.
• [27] Hussien AG, Amin M, Wang M, Liang G, Alsanad A, Gumaei A, et al. Crow search algorithm: theory, recent advances, and applications. IEEE Access 2020;8:173548–65.
• [28] Shekhawat S, Saxena A. Development and applications of an intelligent crow search algorithm based on opposition based learning. ISA Trans 2020;99:210–30.
• [29] Raju AR, Suresh P, Rao RR. Bayesian HCS-based multi-SVNN: A classification approach for brain tumor segmentation and classification using Bayesian fuzzy clustering. Biocybernet Biomed Eng 2018;38(3):646–60.
• [30] Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H. Harris hawks optimization: Algorithm and applications. Fut Gen Comput Syst 2019;97:849–72.
• [31] Moayedi H, Osouli A, Nguyen H, Rashid AS. A novel Harris hawks’ optimization and k-fold cross-validation predicting slope stability. Eng Comput 2019:1–11.
• [32] Jia H, Lang C, Oliva D, Song W, Peng X. Dynamic harris hawks optimization with mutation mechanism for satellite image segmentation. Remote Sensing 2019;11(12):1421.
• [33] Rodríguez-Esparza E, Zanella-Calzada LA, Oliva D, Heidari AA, Zaldivar D, Pérez-Cisneros M, et al. An Efficient Harris Hawks-inspired Image Segmentation Method. Expert Systems with Applications 113428, 2020.
• [34] Zhang Y, Zhou X, Shih PC. Modified Harris Hawks Optimization Algorithm for Global Optimization Problems. Arab J Sci Eng 2020;45(12):10949–74.
• [35] Fan Q, Chen Z, Xia Z. A novel quasi-reflected Harris hawks optimization algorithm for global optimization problems. Soft Comput 2020:1–19.
• [36] Arora S, Singh H, Sharma M, Sharma S, Anand P. A new hybrid algorithm based on grey wolf optimization and crow search algorithm for unconstrained function optimization and feature selection. Ieee Access 2019;7:26343–61.
• [37] Hassanien AE, Rizk-Allah RM, Elhoseny M. A hybrid crow search algorithm based on rough searching scheme for solving engineering optimization problems. J Ambient Intell Humanized Comput 2018:1–25.
• [38] Bao X, Jia H, Lang C. A novel hybrid harris hawks optimization for color image multilevel thresholding segmentation. Ieee Access 2019;7:76529–46.
• [39] Houssein EH, Hosney ME, Oliva D, Mohamed WM, Hassaballah M. A novel hybrid Harris hawks optimization and support vector machines for drug design and discovery. Comput Chem Eng 2020;133 106656.
• [40] Kurtulus E, Yıldız AR, Sait SM, Bureerat S. A novel hybrid Harris hawks-simulated annealing algorithm and RBF-based metamodel for design optimization of highway guardrails. Materials Testing 2020;62(3):251–60.
• [41] Ewees AA, Abd Elaziz M. Performance analysis of chaotic multi-verse harris hawks optimization: a case study on solving engineering problems. Eng Appl Artif Intell 2020;88 103370.
• [42] Abd Elaziz M, Ewees AA, Ibrahim RA, Lu S. Opposition-based moth-flame optimization improved by differential evolution for feature selection. Math Comput Simul 2020;168:48–75.
• [43] Zorarpacı E, Özel SA. A hybrid approach of differential evolution and artificial bee colony for feature selection. Expert Syst Appl 2016;62:91–103.
• [44] Chen X, Tianfield H, Li K. Self-adaptive differential artificial bee colony algorithm for global optimization problems. Swarm Evol Comput 2019;45:70–91.
• [45] Zhang Z, Ding S, Jia W. A hybrid optimization algorithm based on cuckoo search and differential evolution for solving constrained engineering problems. Eng Appl Artif Intell 2019;85:254–68.
• [46] Mafarja MM, Mirjalili S. Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 2017;260:302–12.
• [47] Al-Tashi Q, Kadir SJ, Rais HM, Mirjalili S, Alhussian H. Binary optimization using hybrid grey wolf optimization for feature selection. IEEE Access 2019;7:39496–508.
• [48] Manbari Z, AkhlaghianTab F, Salavati C. Hybrid fast unsupervised feature selection for high-dimensional data. Expert Syst Appl 2019;124:97–118.
• [49] Jia H, Lang C, Oliva D, Song W, Peng X. Hybrid grasshopper optimization algorithm and differential evolution for multilevel satellite image segmentation. Remote Sensing 2019;11(9):1134.
• [50] Thawkar 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. https://doi.org/10.1016/ j.compbiomed.2021.104968 104968.
• [51] Fanizzi A, Lorusso V, Biafora A, Bove S, Comes MC, Cristofaro C, et al. Sentinel Lymph Node Metastasis on Clinically Negative Patients: Preliminary Results of a Machine Learning Model Based on Histopathological Features. Applied Sciences 2021;11(21):10372.
• [52] La Forgia D, Vestito A, Lasciarrea M, Comes MC, Diotaiuti S, Giotta F, et al. Response predictivity to neoadjuvant therapies in breast cancer: A qualitative analysis of background parenchymal enhancement in DCE-MRI. J Personalized Med 2021;11(4):256.
• [53] Comes MC, La Forgia D, Didonna V, Fanizzi A, Giotta F, Latorre A, et al. Early prediction of breast cancer recurrence for patients treated with neoadjuvant chemotherapy: a transfer learning approach on DCE-MRIs. Cancers 2021;13(10):2298.
• [54] Akay MF. Support vector machines combined with feature selection for breast cancer diagnosis. Expert Syst Appl 2009;36(2):3240–7.
• [55] Maglogiannis I, Zafiropoulos E, Anagnostopoulos I. An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers. Appl intell 2009;30 (1):24–36.
• [56] Krishnan MMR, Banerjee S, Chakraborty C, Chakraborty C, Ray AK. Statistical analysis of mammographic features and its classification using support vector machine. Expert Syst Appl 2010;37(1):470–8.
• [57] Azar AT, El-Said SA. Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput Appl 2014;24(5):1163–77.
• [58] Senapati MR, Panda G, Dash PK. Hybrid approach using KPSO and RLS for RBFNN design for breast cancer detection. Neural Comput Appl 2014;24(3):745–53.
• [59] Zheng B, Yoon SW, Lam SS. Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Syst Appl 2014;41 (4):1476–82.
• [60] Phan AV, Nguyen ML, Bui LT. Feature weighting and SVM parameters optimization based on genetic algorithms for classification problems. Appl Intell 2017;46(2):455–69.
• [61] Shahnaz, C., Hossain, J., Fattah, S. A., Ghosh, S., & Khan, A. I. (2017, December). Efficient approaches for accuracy improvement of breast cancer classification using wisconsin database. In 2017 IEEE region 10 humanitarian technology conference (R10-HTC) (pp. 792-797). IEEE.
• [62] Vijayarajeswari R, Parthasarathy P, Vivekanandan S, Basha AA. Classification of mammogram for early detection of breast cancer using SVM classifier and Hough transform. Measurement 2019;146:800–5.
• [63] Kriti JV, Agarwal R. Effect of despeckle filtering on classification of breast tumors using Ultrasound images. Biocybern Biomed Eng 2019;39(2):536–60.
• [64] Alqudah A, Alqudah AM. Sliding window based support vector machine system for classification of breast cancer using histopathological microscopic images. IETE Journal of Research 2019:1–9.
• [65] Demir F. DeepBreastNet: A novel and robust approach for automated breast cancer detection from histopathological images. Biocybernet Biomed Eng 2021;41(3):1123–39.
• [66] Gupta V, Vasudev M, Doegar A, Sambyal N. Breast cancer detection from histopathology images using modified residual neural networks. Biocybernet Biomed Eng 2021;41 (4):1272–87.
• [67] Singh BK. Determining relevant biomarkers for prediction of breast cancer using anthropometric and clinical features: A comparative investigation in machine learning paradigm. Biocybernet Biomed Eng 2019;39(2):393–409.
• [68] Dalwinder S, Birmohan S, Manpreet K. Simultaneous feature weighting and parameter determination of neural networks using ant lion optimization for the classification of breast cancer. Biocybernet Biomed Eng 2020;40(1):337–51.
• [69] Wolpert DH, Macready WG. No free lunch theorems for optimization. IEEE transactions on evolutionary computation 67– 82, 1997.
• [70] Thawkar S. A hybrid model using teaching–learning-based optimization and Salp swarm algorithm for feature selection and classification in digital mammography. J Ambient Intell Hum Comput 2021;1–16. https://doi.org/10.1007/s12652-020- 02662-z.
• [71] Thawkar S, Ingolikar R. Classification of masses in digital mammograms using biogeography-based optimization technique. J King Saud Univ-Comput Inf Sci 2018. https://doi. org/10.1016/j.jksuci.2018.01.004.
• [72] Sameti M, Ward RK, Morgan-Parkes J, Palcic B (1997) A method for detection of malignant masses in digitized mammograms using a fuzzy segmentation algorithm. In 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). IEEE 2: 513-516. https://doi. org/10.1109/IEMBS.1997.757658.
• [73] Li 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):302–13.
• [74] <https://en.wikipedia.org/wiki/Corvus_%28genus%29>.
• [75] <https://en.wikipedia.org/wiki/Hooded_crow>.
• [76] Prior H, Schwarz A, Gunturkun O. Mirror-induced behavior in the magpie. Pica pica 2008;6(8):0060202. https://doi.org/10.1371/journal.pbio.0060202.
• [77] Rincon P. Science/naturel crows and jays top bird IQ scale. BBC News 2005.
• [78] Čepinšek M, Liu SH, Mernik M. Exploration and exploitation in evolutionary algorithms: A survey. ACM computing surveys (CSUR) 2013;45(3):1–33. https://doi.org/10.1145/ 2480741.2480752.
• [79] Yang XS. Nature-inspired metaheuristic algorithms. Luniver press; 2010.
• [80] Cheng HD, Shi XJ, Min R, Hu LM, Cai XP, Du HN. Approaches for automated detection and classification of masses in mammograms. Pattern Recogn 2006;39(4):646–68.
• [81] Saritas I, Ozkan IA, Sert IU. Prognosis of prostate cancer by artificial neural networks. Expert Syst Appl 2010;37 (9):6646–50.
• [82] Cortes C, Vapnik V. Support-vector networks. Machine Learning 1995;20(3):273–97.
• [83] Bowyer K, Kopans D, Kegelmeyer WP, Moore R, Sallam M, Chang K, et al. The digital database for screening mammography. Third international workshop on digital mammography 1996;58:27.
• [84] Heath M, Bowyer K, Kopans D, Kegelmeyer P, Moore R, Chang K, et al. Current status of the digital database for screening mammography. Digital mammography Springer, Dordrecht 1998;457–460. https://doi.org/10.1007/978-94-011-5318-8_75.
• [85] Tharwat A. Classification assessment methods. Appl Comput Inf 2020.
• [86] Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics:159-174.
• [87] Swets JA. Measuring the accuracy of diagnostic systems. Science 1988;240(4857):1285–93.
• [88] Asuncion, A., & Newman, D. (2007). UCI machine learning repository.
• [89] Huang 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):407–14.
• [90] Khan S, Hussain M, Aboalsamh H, Mathkour H, Bebis G, Zakariah M. Optimized Gabor features for mass classification in mammography. Appl Soft Comput 2016;44:267–80.
• [91] Mafarja M, Aljarah I, Faris H, Hammouri AI, Ala’M AZ, Mirjalili S. Binary grasshopper optimisation algorithm approaches for feature selection problems. Expert Syst Appl 2019;117:267–86.
• [92] Ibrahim RA, Ewees AA, Oliva D, Abd Elaziz M, Lu S. Improved salp swarm algorithm based on particle swarm optimization for feature selection. J Ambient Intell Hum Comput 2019;10 (8):3155–69.
• [93] Taradeh M, Mafarja M, Heidari AA, Faris H, Aljarah I, Mirjalili S, et al. An evolutionary gravitational search-based feature selection. Inf Sci 2019;497:219–39.
• [94] Liu N, Qi ES, Xu M, Gao B, Liu GQ. A novel intelligent classification model for breast cancer diagnosis. Inf Process Manage 2019;56(3):609–23.
• [95] Rao H, Shi X, Rodrigue AK, Feng J, Xia Y, Elhoseny M, et al. Feature selection based on artificial bee colony and gradient boosting decision tree. Appl Soft Comput 2019;74:634–42.
• [96] Al-Antari MA, Han SM, Kim TS. Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms. Comput Methods Programs Biomed 2020;196 105584.
• [97] Abdel-Basset M, El-Shahat D, El-henawy I, de Albuquerque VHC, Mirjalili S. A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection. Expert Syst Appl 2020;139. https://doi.org/10.1016/j.eswa.2019.112824 112824.
• [98] Lbachir IA, Daoudi I, Tallal S. Automatic computer-aided diagnosis system for mass detection and classification in mammography. Multimedia Tools Appl 2021;80(6):9493–525.
• [99] Chakravarthy SS, Rajaguru H. Automatic detection and classification of mammograms using improved extreme learning machine with deep learning. IRBM 2022;43(1):49–61.

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)