Extraction of fuzzy rules at different concept levels related to image features of mammography for diagnosis of breast cancer

• Autorzy: Goudarzi M. , Maghooli K.

Identyfikatory

DOI

10.1016/j.bbe.2018.09.002

• Języki publikacji: EN

Abstrakty

EN

Mammography is an inexpensive and non-invasive method through which one can diagnose breast cancer in its early stages. As these images need interpretation by a radiologist, this may develop some problems due to fatigue, repetition, and need for a great deal of attention to details and other factors. Thus, a method capable of diagnosing breast cancer should be employed to help physicians in this regard. In this paper, The mini Mammographic Image Analysis Society (mini-MIAS) database of mammograms is used. The aim is to distinguish between normal and abnormal classes. In the preprocessing stage, noise removal, removal of labels of images, heightening the contrast, and ROI segmentation are performed, and then compactness, entropy, mean, and smoothness are extracted from the images. In addition to classification, we have come to a new approach in order to create a complete knowledge base, which then we use this knowledge base for classification. We have a comprehensive knowledge base which covers all the conceptual levels. The extracted features are referred to as fuzzy classifiers through the look-up table method. And, for evaluation of the results, the 10-fold method is used. Discretization operations are performed on training data across 2, 3, and 4 levels to develop concept hierarchy. Concept hierarchies reduce the data by replacing low-level concepts with higher-level concepts and the outcome is more meaningful and easier to interpret. Eventually, Bagging algorithm is used for finding out the majority vote and the final result of the discretization levels. The obtained accuracy is 89.37 ± 6.62.

Słowa kluczowe

EN

mammography; breast cancer; feature extraction; fuzzy rule extraction; lookup table method; bagging algorithm

PL

mammografia; rak sutka; ekstrakcja cech; ekstrakcja reguł rozmytych; tablica przeglądowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2018
• Tom: Vol. 38, no. 4
• Strony: 1004--1014
• Opis fizyczny: Bibliogr. 29 poz., rys., tab., wykr.

Twórcy

autor

Goudarzi M.

• Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

autor

Maghooli K.

• Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

Bibliografia

• [1] http://www.who.int/en, last accessed March of 2018.
• [2] Tsochatzidis L, Zagoris K, Arikidis N, Karahaliou A, Costaridou L, Pratikakis I. Computer-aided diagnosis of mammographic masses based on a supervised content-based image retrieval approach. Pattern Recogn 2017;71:106–17.
• [3] Xie W, Li Y, Ma Y. Breast mass classification in digital mammography based on extreme learning machine. Neurocomputing 2016;173:930–41.
• [4] Alirezazadeh P, Hejrati B, Monsef-Esfehani A, Fathi A. Representation learning-based unsupervised domain adaptation for classification of breast cancer histopathology images. Biocybern Biomed Eng 2018;38(3):671–83.
• [5] Kitanovski I, Jankulovski B, Dimitrovski I, Loskovska S. Comparison of feature extraction algorithms for mammography images. 4th International Congress on Image and Signal Processing (CISP), vol. 2. 2011. pp. 888–92.
• [6] Dora L, Agrawal S, Panda R, Abraham A. Optimal breast cancer classification using Gauss–Newton representation based algorithm. Expert Syst Appl 2017;85:134–45.
• [7] Bhardwaj A, Tiwari A. Breast cancer diagnosis using genetically optimized neural network model. Expert Syst Appl 2015;42(10):4611–20.
• [8] Hassan R, Hossain M, Begg RK, Ramamohanarao K, Morsi Y. Breast-cancer identification using HMM-fuzzy approach. Comput Biol Med 2010;40(3):240–51.
• [9] Şahan S, Polat K, Kodaz H, Güneş S. A newhybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis. Comput Biol Med 2007;37(3):415–23.
• [10] Keleş A, Keleş A, Yavuz U. Expert system based on neuro-fuzzy rules for diagnosis breast cancer. Expert Syst Appl 2011;38(5):5719–26.
• [11] Karabatak M, Ince C. An expert system for detection of breast cancer based on association rules and neural network. Expert Syst Appl 2009;36(2):3465–9.
• [12] Miranda GHB, Felipe JC. Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization. Comput Biol Med 2015;64:334–46.
• [13] Nilashi M, Ibrahim O, Ahmadi H, Shahmoradi L. A knowledge-based system for breast cancer classification using fuzzy logic method. Telemat Inform 2017;34(4):133–44.
• [14] Ghosh S, Biswas S, Sarkar D, Sarkar PP. A novel Neuro-fuzzy classification technique for data mining. Egyptian Inform J 2014;15(3):129–47.
• [15] Töpfer S, Nelles O, Isermann R. Comparison of a hierarchically constructed neural network and a hierarchical look-up table. IFAC Proc Vol 2000;33(15):67–72.
• [16] Suckling J, Boggis CRM, Hutt I, Astley S, Betal D, Cerneaz N, et al. The mini-MIAS database of mammograms. Essex, v1; http://peipa.essex.ac.uk/info/mias.html.
• [17] Al-shamlan H, El-zaart A. Feature extraction values for breast cancer mammography images. International conference on bioinformatics and biomedical technology. 2010. pp. 335–40.
• [18] Radovic M, Djokovic M, Peulic A, Filipovic N.IEEE 13th international conference on bioinformatics and bioengineering. Application of data mining algorithms for mammogram classification 2013;1–4.
• [19] Tweed T, Miguet S.16th international conference on pattern recognition, vol. 2. Automatic detection of regions of interest in mammographies based on a combined analysis of texture and histogram 2002;448–52.
• [20] Gonzalez RC, Woods RE. Digital image processing. Pearson; 2007.
• [21] Cheng E, Xie N, Ling H, Bakic PR, Maidment ADA, Megalooikonomou V. Mammographic image classification using histogram intersection. IEEE international symposium on biomedical imagine: from nano to macro. 2010. pp. 197–200.
• [22] Pak F, Rashidy Kanan H, Alikhassi A. Breast cancer detection and classification in digital mammography based on non-subsampled contourlet transform (NSCT) and super resolution. Comput Methods Program Biomed 2015;122(2):89–107.
• [23] Sheshadri HS, Kandaswamy A. Experimental investigation on breast tissue classification based on statistical feature extraction of mammograms. Comput Med Imag Graph 2007;31(1):46–8.
• [24] Espinosa J, Vandewalle J, Wertz V. Constructing fuzzy models from input–output data. Fuzzy logic. Identification and predictive control. Springer; 2005. p. 21–58.
• [25] Pawar MM, Talbar SN. Genetic Fuzzy System (GFS) based wavelet co-occurrence feature selection in mammogram classification for breast cancer diagnosis. Perspect Sci 2016;8:247–50.
• [26] De Oliveira Silva LC, Barros AK, Lopes MV. Detecting masses in dense breast using independent component analysis. Artif Intell Med 2017;80:29–38.
• [27] Li JB. Mammographic image based breast tissue classification with kernel self-optimized fisher discriminant for breast cancer diagnosis. J Med Syst 2012;36(4):2235–44.
• [28] Gardezi SJS, Faye I, Eltoukhy MM. Analysis of mammogram images based on texture features of curvelet sub-bands. Fifth international conference on graph and image proceedings, vol. 9069. 2014. p. 906924.
• [29] Dhahbi S, Barhoumi W, Zagrouba E. Breast cancer diagnosis in digitized mammograms using curvelet moments. Comput Biol Med 2015;64:79–90.