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Thirdly, the Welch\u2019s t-test was utilized to select important features. Fourthly, the multi-layer perceptron was used as the classifier. Finally, we proposed a novel chaotic adaptive real-coded biogeography-based optimization to train the classifier. We implemented 10-fold cross-validation for statistical analysis. The experimental results showed our method selected in total 23 distinguishing features, and yielded a sensitivity of 92.54%, a specificity of 92.50%, a precision of 92.50%, and an accuracy of 92.52%. This proposed system performs better than five state-of-the-art methods. It is effective in abnormal breast detection.<\/jats:p>","DOI":"10.1177\/0037549716667834","type":"journal-article","created":{"date-parts":[[2016,9,12]],"date-time":"2016-09-12T22:09:03Z","timestamp":1473718143000},"page":"873-885","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":69,"title":["Smart detection on abnormal breasts in digital mammography based on contrast-limited adaptive histogram equalization and chaotic adaptive real-coded biogeography-based optimization"],"prefix":"10.1177","volume":"92","author":[{"given":"Yudong","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Nanjing Normal University, China"},{"name":"Hunan Provincial Key Laboratory of Network Investigational Technology, Hunan Policy Academy, China"},{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, 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