Main Article Content
Breast cancer is one of the most common diseases among women in several countries. One of the most common methods to diagnose breast cancer is mammography. In this study, we propose a classification study to differentiate benign and malignant breast tumors based on mammogram image. The proposed system includes five major steps, i.e. preprocessing, histogram equalization, convolution, feature extraction, and classification. Image is cropped using region of interest (ROI) at preprocessing stage. In this study, we perform image contrast quality enhancement of the mammogram to view the breast cancer better. Image contrast enhancement uses histogram equalization and Gaussian filter. Gray-Level Co-Occurrence Matrix (GLCM) is used to extract the mammogram features. There are five features used i.e. entropy, correlation, contrast, homogeneity, and variance. The last step is to classify using naïve Bayes classifier (NBC) and k-nearest neighbor (KNN). Based on the hypothesis, the accuracy of NBC method is 90% and the accuracy of KKN method is 87.5%. So, the mammogram image contrast enhancement is well performed.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Please find the rights and licenses in the Journal of Information Technology and Computer Engineering (JITCE).
The non-commercial use of the article will be governed by the Creative Commons Attribution license as currently displayed on Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
2. Author(s)’ Warranties
The author(s) warrants that the article is original, written by stated author(s), has not been published before, contains no unlawful statements, does not infringe the rights of others, is subject to copyright that is vested exclusively in the author and free of any third party rights, and that any necessary permissions to quote from other sources have been obtained by the author(s).
3. User Rights
JITCE adopts the spirit of open access and open science, which disseminates articles published as free as possible under the Creative Commons license. JITCE permits users to copy, distribute, display, and perform the work for non-commercial purposes only. Users will also need to attribute authors and JITCE on distributing works in the journal.
4. Rights of Authors
Authors retain the following rights:
- Copyright, and other proprietary rights relating to the article, such as patent rights,
- the right to use the substance of the article in future own works, including lectures and books,
- the right to reproduce the article for own purposes,
- the right to self-archive the article.
- the right to enter into separate, additional contractual arrangements for the non-exclusive distribution of the article's published version (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal (Journal of Information Technology and Computer Engineering).
If the article was jointly prepared by other authors; upon submitting the article, the author is agreed on this form and warrants that he/she has been authorized by all co-authors on their behalf, and agrees to inform his/her co-authors. JITCE will be freed on any disputes that will occur regarding this issue.
By submitting the articles, the authors agreed that no fees are payable from JITCE.
JITCE will publish the article (or have it published) in the journal if the article’s editorial process is successfully completed and JITCE or its sublicensee has become obligated to have the article published. JITCE may adjust the article to a style of punctuation, spelling, capitalization, referencing and usage that it deems appropriate. The author acknowledges that the article may be published so that it will be publicly accessible and such access will be free of charge for the readers.
 D. Daye et al., “Mammographic Parenchymal Patterns as an Imaging Marker of Endogenous Hormonal Exposure,” Acad. Radiol., vol. 20, no. 5, pp. 635–646, May 2013.
 Tintu and Paulin, “Detect Breast Cancer using Fuzzy C means Techniques in Wisconsin Prognostic Breast Cancer (WPBC) Data Sets,” Int. J. Comput. Appl. Technol. Res., pp. 614–617, Sep. 2013.
 R. Vijayarajeswari, P. Parthasarathy, S. Vivekanandan, and A. A. Basha, “Classification of mammogram for early detection of breast cancer using SVM classifier and Hough transform,” Measurement, vol. 146, pp. 800–805, Nov. 2019.
 M. Karabatak, “A new classifier for breast cancer detection based on Naïve Bayesian,” Measurement, vol. 72, pp. 32–36, Aug. 2015.
 T. Celik, “Two-dimensional histogram equalization and contrast enhancement,” Pattern Recognit., vol. 45, no. 10, pp. 3810–3824, 2012.
 Y. T. Chang, J. T. Wang, W. H. Yang, and X. W. Chen, “Contrast Enhancement in Palm Bone Image Using Quad-Histogram Equalization,” pp. 1091–1094, 2014.
 S. Timp and N. Karssemeije, “"Interval change analysis to improve computer aided detection in mammography,” Med. Image Anal., vol. 10, no. 1, pp. 82–95, 2006.
 R. Gonzales and R. Wood, Digital Image Processing. Prentice-Hall, Inc., United State, America, 2007.
 F. S. Mohamad, A. A. Manaf, and S. Chuprat, “Nearest Neighbor For Histogram-based Feature Extraction,” Procedia Comput. Sci., vol. 4, pp. 1296–1305, 2011.
 S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 3 edition. Upper Saddle River: Pearson, 2009.