Abstract
Breast cancer is taken into account as one of the foremost common kinds of cancer occurring in women worldwide. This work is intended to develop an automated classification system in order to classify the breast tumor as benign or malignant. An online database with 645 benign and 1370 malignant histopathological images with 40X magnification is used in this work. A total of 174 color features based on wavelet transform are extracted. To work out the foremost relevant attributes for classifying breast tumors, feature selection is carried out. The best subset is used for feature choice (wrapper), whereas prime 18 features are used for filter-based methods. A back-propagation artificial neural network (BPANN) is used as the classifier. Various performance measures like accuracy, sensitivity, specificity, etc., are used for evaluating the performance of BPANN classifier after applying different feature selection techniques. Classification accuracy of 98.56% with tenfold data division protocol is obtained when all 174 color wavelet transform-based features were used for classification. However, by using different feature selection techniques, the highest accuracy is obtained as 95.83% for the wrapper approach when 18 relevant features were used.
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References
Al Rahhal MM (2018) Breast cancer classification in histopathological images using convolutional neural network. Breast Cancer 9(3)
Anusha MV, Reddy MVU, Ramashri DT (2014) Content based image retrieval using color moments and texture, vol 3, no 2, pp 2812–2815
Araujo T, Aresta G, Castro E, Rouco J, Aguiar P, Eloy C, Polonia A, Campilho A (2017) Classification of breast cancer histology images using convolutional neural networks. PLoS ONE 12(6):e0177544
Aswathy MA, Jagannath M (2017) Detection of breast cancer on digital histopathology images: present status and future possibilities. Inform Med Unlocked 8:74–79
Bardou D, Zhang K, Ahmad SM (2018) Classification of breast cancer based on histology images using convolutional neural networks. IEEE Access 6:24680–24693
Bayramoglu N, Kannala J, Heikkila J (2016) Deep learning for magnification independent breast cancer histopathology image classification. In: 2016 23rd international conference on pattern recognition (ICPR). IEEE, pp 2440–2445
Belsare AD, Mushrif MM, Pangarkar MA, Meshram N (2015) Classification of breast cancer histopathology images using texture feature analysis. In: TENCON 2015–2015 IEEE Region 10 Conference. IEEE, pp 1–5
Breast cancer—Statistics and facts statista. https://www.statista.com/topics/4337/breast-cancer-in-the-us/. Accessed 09 Oct 2018.
Ghazali KH, Mansor MF, Mustafa MM, Hussain A (2007) Feature extraction technique using discrete wavelet transform for image classification. In: SCOReD 2007. 5th student conference on research and development, 2007. IEEE, pp 1–4
Golatkar A, Anand D, Sethi A (2018) Classification of breast cancer histology using deep learning. In: International conference image analysis and recognition. Springer, Cham, pp 837–844
Haykin S (1994) Neural networks: a comprehensive foundation, 2nd ed. Prentice Hall PTR
Kahya MA, Al-Hayani W, Algamal ZY (2017) Classification of breast cancer histopathology images based on adaptive sparse support vector machine. J Appl Math Bioinform 7(1):49–69
Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97:273–324
Kumar S, Chauhan A (2013) Feature extraction techniques based on color images. In: Special conference issue: National conference on cloud computing & big data, vol 214, pp 208
Motlagh NH, Jannesary M, Aboulkheyr H, Khosravi P, Elemento O, Totonchi M, Hajirasouliha I (2018) Breast cancer histopathological image classification: a deep learning approach. BioRxiv 242818
Nahid AA, Mehrabi MA, KongY (2018) Histopathological breast cancer image classification by deep neural network techniques guided by local clustering. BioMed Res Int
Phyu TZ, Oo NN (2016) Performance comparison of feature selection methods. In: MATEC web of conferences, vol 42, p 06002. EDP Sciences, pp 1–4
Porkodi R (2014) Comparison of filter based feature selection algorithms: an overview. Int J Innov Res Technol Sci (IJIRTS) 2(2):108–113
Rajan RK, Swathy C.K (2017) Breast cancer detection using histopathology and thermography images. Int J Innov Res Sci, Eng Technol 6(4):6868–6875
Samah AA, Fauzi MFA, Mansor S (2017) Classification of benign and malignant tumors in histopathology images. In: 2017 IEEE international conference on signal and image processing applications (ICSIPA). IEEE, pp 102–106
Spanhol FA, Oliveira LS, Caroline Petitjean C, Heutte L (2016) A dataset for breast cancer histopathological image classification. IEEE Trans Biomed Eng 63(7):1455–1462
Wei B, Han Z He X, Yin Y (2017) Deep learning model based breast cancer histopathological image classification. In: 2017 IEEE 2nd international conference on cloud computing and big data analysis (ICCCBDA). IEEE, pp 348–353
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Atrey, K., Singh, B.K., Bodhey, N.K. (2021). Feature Selection for Classification of Breast Cancer in Histopathology Images: A Comparative Investigation Using Wavelet-Based Color Features. In: Rizvanov, A.A., Singh, B.K., Ganasala, P. (eds) Advances in Biomedical Engineering and Technology. Lecture Notes in Bioengineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-6329-4_30
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DOI: https://doi.org/10.1007/978-981-15-6329-4_30
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