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Second, an embedded feature selection method is developed with an expert classifier to detect calcified objects in the segmented artery with great accuracy. Finally, the Agatston scoring method is utilized to quantify the level of coronary artery calcium plaque. Coronary CT images from the AS+CT scanner with a slice thickness of 3 mm were obtained from clinical practice. Experimental results demonstrate that our proposed method improves the accuracy of lesion detection for better treatment planning.<\/jats:p>","DOI":"10.4018\/ijiit.2017070102","type":"journal-article","created":{"date-parts":[[2017,5,12]],"date-time":"2017-05-12T10:11:15Z","timestamp":1494583875000},"page":"15-36","source":"Crossref","is-referenced-by-count":3,"title":["An Efficient Coronary Disease Diagnosis System Using Dual-Phase Multi-Objective Optimization and Embedded Feature Selection"],"prefix":"10.4018","volume":"13","author":[{"family":"Priyatharshini R.","sequence":"first","affiliation":[{"name":"Easwari Engineering College, Department of Information Technology, Chennai, India"}]},{"family":"Chitrakala S.","sequence":"additional","affiliation":[{"name":"Anna University, Department of Computer Science and Engineering, Chennai, India"}]}],"member":"2432","reference":[{"key":"IJIIT.2017070102-0","unstructured":"Anand, N. (2016). 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