{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:48:05Z","timestamp":1776811685338,"version":"3.51.2"},"reference-count":14,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2023,5,30]]},"abstract":"<jats:p>Breast cancer is the most frequent cancer and the leading cause of death among females. Diagnosis mass from mammogram correctly can reduce the unnecessary biopsy to a large extent. In this paper, we present a novel mammogram classification method combining the Random Forest and the Locally Linear Embedding (LLE) dimensionality reduction algorithm for texture features. The proposed method consists of three stages. In the first stage, preprocessing is performed to enhance the contrast and suppress the noise of the ROI images. Then, the sixteen-dimensional texture features are extracted from Grey Level Co-occurrence Matrix (GLCM) as the input dataset of LLE and being mapped into a five-dimensional subspace. Finally, a Random Forest classifier is investigated for the mammogram classification and compared with the other four classifiers (SVM, KNN, Logistic Regression, MLPC). The experimental results show that the Random Forest classifier outperforms than the others, with an average accuracy of 92.87% and the AUC value of 0.99, that indicates that the combination of LLE algorithm and Random Forest classifier is a promising method for the mammogram classification.<\/jats:p>","DOI":"10.3233\/jcm-226669","type":"journal-article","created":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T11:46:36Z","timestamp":1673955996000},"page":"1537-1545","source":"Crossref","is-referenced-by-count":2,"title":["Texture feature dimensionality reduction-based mammography classification using Random Forest"],"prefix":"10.66113","volume":"23","author":[{"given":"Xuejun","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer and Electronic Information, Guangxi University, Nanning, Guangxi, China"},{"name":"Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning, Guangxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Susu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Electronic Information, Guangxi University, Nanning, Guangxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaohui","family":"Bu","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning, Guangxi, China"},{"name":"School of Foreign Language, Guangxi University, Nanning, Guangxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangdi","family":"Ma","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning, Guangxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ju","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer and Electronic Information, Guangxi University, Nanning, Guangxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55691","reference":[{"issue":"6","key":"10.3233\/JCM-226669_ref1","first-page":"394","article-title":"Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"68","author":"Bray","year":"2018","journal-title":"CA: A Cancer Journal for Clinicians."},{"issue":"3","key":"10.3233\/JCM-226669_ref2","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.clbc.2012.12.002","article-title":"Favorable changes in serum estrogens and other biologic factors after weight loss in breast cancer survivors who are overweight or obese","volume":"13","author":"Rock","year":"2013","journal-title":"Clinical Breast Cancer."},{"issue":"3","key":"10.3233\/JCM-226669_ref3","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/s10549-006-9252-6","article-title":"Variation in false-positive rates of mammography reading among 1067 radiologists: A population-based assessment","volume":"100","author":"Tan","year":"2006","journal-title":"Breast Cancer Research and Treatment."},{"issue":"1","key":"10.3233\/JCM-226669_ref4","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/1471-2342-12-22","article-title":"Is single reading with computer-aided detection (CAD) as good as double reading in mammography screening? 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