Title: Analysis of texture for classification of breast cancer on mammogram images

Authors: H.A. Nugroho; H.R. Fajrin; I. Soesanti; R.L. Budiani

Addresses: Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Jl. Grafika 2, Kampus UGM, Yogyakarta, Indonesia ' Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Jl. Grafika 2, Kampus UGM, Yogyakarta, Indonesia ' Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Jl. Grafika 2, Kampus UGM, Yogyakarta, Indonesia ' Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Jl. Grafika 2, Kampus UGM, Yogyakarta, Indonesia

Abstract: Breast cancer is a top cancer among women in the world. In conventional method, breast cancer can be detected by a medical expertise observation on patient's mammogram images. However, this method could lead to misdiagnose in distinguishing an interest object with naked eyes due to low quality of images. This research aims to classify mammogram images into three classes, i.e. normal, benign and malignant based on texture features. Some pre-processing techniques were involved, including removing the artefacts, cropping breast area, contrast enhancement and smoothing with median filter. Afterwards, some texture features were extracted followed by classification process by using multi-layer perceptron (MLP) classifier. Classification of normal and abnormal successfully achieved an accuracy of 98.33%, sensitivity of 100% and specificity of 97.5%. Whereas, for classification of three classes (normal, benign and malignant) achieved an accuracy of 90%, sensitivity of 85% and specificity of 87.5%.

Keywords: breast cancer; texture feature; contrast limited adaptive histogram equalisation; CLAHE; grey level co-occurrence matrices; GLCM; multi-layer perceptron; MLP.

DOI: 10.1504/IJMEI.2018.095088

International Journal of Medical Engineering and Informatics, 2018 Vol.10 No.4, pp.382 - 391

Received: 27 Oct 2016
Accepted: 19 May 2017
Published online: 01 Oct 2018 *

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