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The best-fit features are first trained and verified for their similarity. The clustering process recurrently groups the feature matched pixels into clusters and updates the centroid based on further classifications. In this classification process, the uncertain pixels are identified and mitigated in the tumor detection analysis. The best-fit features are used to train local search instances in the BOA process, which influences the similar pixel grouping in the uncertainty detection process. The proposed BOAKMC improves accuracy and precision by 10.2% and 13.39% and reduces classification failure and time by 11.29% and 11.52%, respectively. <\/jats:p>","DOI":"10.1142\/s0218001422400018","type":"journal-article","created":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T09:00:31Z","timestamp":1661936431000},"source":"Crossref","is-referenced-by-count":7,"title":["Detecting Lung Cancer Region from CT Image Using Meta-Heuristic Optimized Segmentation Approach"],"prefix":"10.1142","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1647-8266","authenticated-orcid":false,"given":"Pethuraj Mohamed","family":"Shakeel","sequence":"first","affiliation":[{"name":"Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka 76100, Malaysia"}]},{"given":"Burhanuddin","family":"bin Mohd Aboobaider","sequence":"additional","affiliation":[{"name":"Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka 76100, Malaysia"}]},{"given":"Lizawati Binti","family":"Salahuddin","sequence":"additional","affiliation":[{"name":"Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka 76100, Malaysia"}]}],"member":"219","published-online":{"date-parts":[[2022,11,7]]},"reference":[{"key":"S0218001422400018BIB001","doi-asserted-by":"crossref","first-page":"3391","DOI":"10.1007\/s10489-020-02046-y","volume":"51","author":"Akter O.","year":"2020","journal-title":"Appl. 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