Study and evaluation of feature vector optimization and classic methods in automatic breast cancer detection

Document Type : Research Paper


Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran


Breast cancer is known to be among the most prevalent cause of mortality among women. Since early breast cancer diagnosis increases survival chances, the development of a system with a highly accurate output to detect suspicious masses in mammographic images is of great significance. Thus, many studies have focused on the development of methods with favorable performance and acceptable accuracy to detect cancerous masses, proposed various techniques to diagnose breast cancer, and compared their accuracies. Most previous studies have used composite selection and feature reduction techniques to detect breast cancer and accelerate its treatment; however, most have failed to reach the desired accuracy due to the selection of ineffective features and the lack of a proper analytical method for the features. The present study reviews the methods proposed to detect breast cancer so far and analyzes the process of feature vector optimization techniques as well as the normal/abnormal and benign/malignant mass classification.


[1] M. Abdar and V. Makarenkov, CWV-BANN-SVM ensemble learning classifier for an accurate diagnosis of breast cancer, Measurement 146 (2019), 557–570.
[2] E. Alickovic and A. Subasi, Normalized neural networks for breast cancer classification, CMBEBIH 2019 Int. Conf. Med. Bio. Engin., 16-18 May 2019, Banja Luka, Bosnia and Herzegovina. Springer International Publishing, 2020.
[3] D.A. Aljawad, E. Alqahtani, A.L.K Ghaidaa, N. Qamhan, N. Alghamdi, S. Alrashed, J. Alhiyafi and S.O. Olatunji, Breast cancer surgery survivability prediction using Bayesian network and support vector machines, Int. Conf. Inf. Health Technol. (ICIHT), IEEE, 2017, pp. 1–6.
[4] L.M. Alnemer, L. Rajab and I. Aljarah, Conformal prediction technique to predict breast cancer survivability, Int. J. Adv. Sci. Technol. 96 (2016), 1–10.
[5] T.A. Assegie, An optimized K-Nearest Neighbor based breast cancer detection, J. Robotics Control 2 (2021), no. 3, 115–118.
[6] K. Avinash, M.B. Bijoy and P.B. Jayaraj, Early detection of breast cancer using support vector machine with sequential minimal optimization, Adv. Comput. Intel. Engin.: Proc. ICACIE 2018, Volume 1. Springer Singapore, 2020, pp. 13–24.
[7] W. Ayadi, W. Elhamzi, I. Charfi and M. Atri, A hybrid feature extraction approach for brain MRI classification based on Bag-of-words, Biomed. Signal Process. Control 48 (2019), 144–152.
[8] N.E. Benzebouchi, N. Azizi and K. Ayadi, A computer-aided diagnosis system for breast cancer using deep convolutional neural networks, Comput. Intel. Data Min.: Proc. Int. Conf. CIDM 2017, Springer Singapore, 2019.
[9] R. Chaieb and K. Kalti, Feature subset selection for classification of malignant and benign breast masses in digital mammography, Pattern Anal. Appl. 22 (2019), 803–829.
[10] S. Dhahbi, W. Barhoumi and E. Zagrouba, Breast cancer diagnosis in digitized mammograms using curvelet moments, Comput. Bio. Med. 64 (2015), 79–90.
[11] Y. Gherghout, Y. Tlili and L. Souici, Classification of breast mass in mammography using anisotropic diffusion filter by selecting and aggregating morphological and textural features. Evolv. Syst. 12 (2021), 273–302.
[12] A.U. Haq, J.P. Li, A. Saboor, J. Khan, S. Wali, S. Ahmad, A. Ali, G.A. Khan and W. Zhou, Detection of breast cancer through clinical data using supervised and unsupervised feature selection techniques, IEEE Access 9 (2021), 22090–22105.
[13] H. Hosseinzadeh, Automated skin lesion division utilizing Gabor filters based on shark smell optimizing method, Evolv. Syst. 11 (2020), no. 4, 589–598.
[14] H. Hosseinzadeh and M. Sedaghat, Brain image clustering by wavelet energy and CBSSO optimization algorithm, J. Mind Med. Sci. 6 (2019), no. 1, 110–120.
[15] M.W. Huang, C.W. Chen, W.C. Lin, S.W. Ke and C.F. Tsai, SVM and SVM ensembles in breast cancer prediction, PloS one 12 (2019), no. 1, e0161501.
[16] L. Hussain, W. Aziz, S. Saeed, S. Rathore and M. Rafique, Automated breast cancer detection using machine learning techniques by extracting different feature extracting strategies, 17th IEEE Int. Conf. Trust Secur. Privacy Comput. Commun. /12th IEEE Int. Conf. Big Data Sci. Engin. (TrustCom/BigDataSE). IEEE, 2018, pp. 327–331.
[17] S. Jitaree, A. Phinyomark, P. Phukpattaranont and P. Boonyapiphat, Classifying breast cancer regions in microscopic image using texture features, 13th Int. Conf. Electric. Engin. Electronics Comput. Telecommun. Inf. Technol. (ECTI-CON), IEEE, 2016.
[18] M. Karabatak, A new classifier for breast cancer detection based on Na¨ıve Bayesian, Measurement 72 (2015), 32–36.
[19] R. Karthiga, K. Narasimhan and G. Usha, Breast cancer diagnosis using curvelet and regional features, Int. Conf. Comput. Commun. Inf. (ICCCI). IEEE, 2019, pp. 1–5.
[20] S. Kaymak, A. Helwan and D. Uzun, Breast cancer image classification using artificial neural networks, Procedia Comput. Sci. 120 (2017), 126–131.
[21] S. Khan, N. Islam, Z. Jan, I.U. Din and J.J.C. Rodrigues, A novel deep learning based framework for the detection and classification of breast cancer using transfer learning, Pattern Recogn. Lett. 125 (2019), 1–6.
[22] Y.C. Kuo, W.C. Lin, S.C. Hsu and A.C. Cheng, Mass detection in digital mammograms system based on PSO algorithm, Int. Symp. Comput. Consumer Control IEEE, 2014, pp. 663–668.
[23] J.N.K. Liu, Y.L. He, X.Z. Wang and Y.X. Hu, A comparative study among different kernel functions in flexible naıve Bayesian classification, Proc. Int. Conf. Machine Learn. Cybernet. 2 (2011), 638—643.
[24] N. Liu, E.S. Qi, M. Xu, B. Gao and G.Q. Liu, A novel intelligent classification model for breast cancer diagnosis, Inf. Process. Manag. 56 (2019), no. 3, 609–623.
[25] X. Liu, J. Tang, Mass classification in mammograms using selected geometry and texture features, and a new SVM-based feature selection method, IEEE Syst. J. 8 (2013), no. 3, 910–920.
[26] S. Liu, J. Zeng, H. Gong, H. Yang, J. Zhai, Y. Cao and X. Ding, Quantitative analysis of breast cancer diagnosis using a probabilistic modelling approach, Comput. Bio. Med. 92 (2018), 168–175.
[27] A.O.I. Malagelada, Automatic Mass Segmentation in Mammographic Images, Ph.D. Thesis, Universitat de Girona, 2007.
[28] J.G. Melekoodappattu and P.S. Subbian, Automated breast cancer detection using hybrid extreme learning machine classifier, J Ambient Intell Human Comput (2020).
[29] C.E.F. Matos, J.C. Souza, J.O.B. Diniz, G.B. Junior, A.C. de Paiva, J.D.S. de Almeida, S.V. da Rocha and A.C. Silva, Diagnosis of breast tissue in mammography images based local feature descriptors, Multimedia Tools Appl. 78 (2019), 12961–12986.
[30] S.H. Mohan and T.R. Mahesh, Particle swarm optimization based contrast limited enhancement for mammogram images, 7th Int. Conf. Intell. Syst. Control (ISCO), 2013, pp. 384–388.
[31] S. Pramanik, D. Banik, D. Bhattacharjee and M. Nasipuri, AA computer-aided hybrid framework for early diagnosis of breast cancer, Adv. Comput. Syst. Secur. 8 (2019), 111–124.
[32] A.I. Pritom, M.A.R. Munshi, S.A. Sabab and S. Shihab, Predicting breast cancer recurrence using effective classification and feature selection technique, 19th Int. Conf. Comput. Inf. Technol. (ICCIT), IEEE, 2016, pp. 310–314.
[33] A.H. Osman, An enhanced breast cancer diagnosis scheme based on two-step-SVM technique, Int. J. Adv. Comput. Sci. Appl. 8 (2017), no. 4, 158–165.
[34] X. Qi, L. Zhang, Y. Chen, Y. Pi, Y. Chen, Q. Lv, and Z. Yi, Automated diagnosis of breast ultrasonography images using deep neural networks, Med. Image Anal. 52 (2019), 185–198.
[35] S. Sadi, A. Maleki, R. Hashemi, Z. Panbechi and K. Chalabi, Comparison of data mining algorithms in the diagnosis of type II diabetes, Int. J. Comput. Sci. Appl. 5 (2015), no. 5, 1–12.
[36] M.S. Salama, A.S. Eltrass and H.M. Elkamchouchi, An improved approach for computer-aided diagnosis of breast
cancer in digital mammography, IEEE Int. Symp. Med. Measur. Appl. (MeMeA), IEEE, 2018, pp. 1–5.
[37] J. Suckling, The mammographic image analysis society digital mammogram database, Exerpta Medical Int. Cong. Ser. 1069 (1994), 375–378.
[38] R. Vijayarajeswari, P. Parthasarathy, S. Vivekanandan and A. Alavudeen Bash, Classification of mammogram for early detection of breast cancer using SVM classifier and Hough transform, Measurement 146 (2019), 800–805.
[39] Z. Wang, M. Li, H. Wang, H. Jiang, Y. Yao, H. Zhang and J. Xin, Breast cancer detection using extreme learning machine based on feature fusion with CNN deep features, IEEE Access 7 (2019), 105146–105158.
[40] D. Wang and Ph.H. Lin Shi, Automatic detection of breast cancers in mammograms using structured support vector machines, Neurocomputing 72 (2009), 13–15.
[41] F. Wang, S. Zhang and L.M. Henderson, Adaptive decision-making of breast cancer mammography screening: A heuristic-based regression model, Omega 76 (2018), 70–84.
[42] H. Wang, B. Zheng, S.W. Yoon and H. Sang Ko, A support vector machine-based ensemble algorithm for breast cancer diagnosis, Eur. J. Oper. Res. 267 (2018), no. 2, 687–699.
[43] J.L. Weidong Tangb and H. Hosseinzadeh, Developed multiple-layer perceptron neural network based on developed search and rescue optimizer to predict iron ore price volatility: A case study, ISA Trans. 130 (2022), 420–432.
[44] Y. Xiao, J. Wu, Z. Lin and X. Zhao, Breast cancer diagnosis using an unsupervised feature extraction algorithm based on deep learning, 37th Chinese Control Conf. (CCC), IEEE, 2018, pp. 9428–9433.
[45] B. Zheng, S.W. Yoon and S.S. Lam, Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms, Expert Syst. Appl. 41 (2014), 1476—1482.
[47] The Mammographic Image Analysis Society (MIAS), Internet site address:
[48] University of South Florida Digital Mammography Home Page, Available At: y/Dhatabase.html 
Volume 15, Issue 1
January 2024
Pages 17-30
  • Receive Date: 15 July 2022
  • Accept Date: 10 January 2023