标题:Pulmonary nodules detection algorithm based on robust cascade classifier for CT images
作者:Li, Xia ;Yang, Yang ;Xiong, Hailiang ;Song, Shangling ;Jia, Hongying
作者机构:[Li, Xia ;Yang, Yang ;Xiong, Hailiang ] School of Information Science and Engineering, Shandong University, Jinan, China;[Song, Shangling ;Jia, Hongyi 更多
会议名称:29th Chinese Control and Decision Conference, CCDC 2017
会议日期:28 May 2017 through 30 May 2017
来源:Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017
出版年:2017
页码:231-235
DOI:10.1109/CCDC.2017.7978097
关键词:AdaBoost Algorithm; Cascade Classifier; CT images; Pulmonary Nodules
摘要:Lung cancer has been the deadliest among all other types of cancer. Our purpose is to propose an efficient method to detect the pulmonary nodules from CT images and classify the nodule into either cancerous (Malignant) or non-cancerous (Benign). We achieve this by framing the problem as a constructing classifier task and exploit data in the form of classifier to learn a mapping from raw data to object classification. In particular, we propose a learning method based on a form of cascade classifier which allows learning in a supervised manner, only based on pulmonary nodule image block extracted from the original CT images without access to around-information annotations. In order to validate our approach, we use a synthetic database to mimic the task of detecting pulmonary nodule automatically from CT images - as commonly encountered in automatic detection of medical images applications - and show that classifier can automatically detect pulmonary nodules from the lungs CT images accurately. The method is able to achieve an overall accuracy of 97.01%. © 2017 IEEE.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028067120&doi=10.1109%2fCCDC.2017.7978097&partnerID=40&md5=6de046cd6dd05391ce120bd3a8f03ca6
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