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华中师范大学学报(自然科学版)  2020, Vol. 54 Issue (6): 935-943    
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基于改进的自适应提升算法的乳腺癌图像识别研究
张红斌1, 邬任重1, 蒋子良1, 武晋鹏1, 袁 天2, 滑 瑾2, 姬东鸿3
1.华东交通大学软件学院, 南昌 330013; 2.华东交通大学信息工程学院, 南昌 330013;3.武汉大学国家网络安全学院, 武汉430072
Breast cancer image recognition based on modified Adaboost algorithm
ZHANG Hongbin1, WU Renzhong1, JIANG Ziliang1, WU Jinpeng1, YUAN Tian2, HUA Jin2, JI Donghong3
1.Software School, East China Jiaotong University, Nanchang 330013, China;2.School of Information Engineering, East China Jiaotong University, Nanchang 330013, China;3.School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China
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摘要 为解决医疗资源不足、就诊量日增等问题,需设计基于计算机的乳腺癌图像识别模型,更高效地辅助病理医生的临床诊断工作.然而,现有算法多采用单类别特征完成识别,未充分发挥特征之间互补性.该文提出改进的自适应提升算法:在SIFT、Gist、HOG、VGG16特征提取基础上,改进有效区域基因选择(Effective Range Based Gene Selection,ERGS)算法,动态计算特征权重;采用自适应提升算法将弱分类器集成为强分类器,并对其输出的预估概率做ERGS加权,实现多特征融合.实验表明:1) 算法识别精准度达86.24%,较最强基线提高3.82%;2) SIFT、Gist、HOG特征之间具有较强互补性,它们有助于准确刻画乳腺癌图像;3) 阳性图像更易识别.
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张红斌
邬任重
蒋子良
武晋鹏
袁 天
滑 瑾
姬东鸿
关键词 乳腺癌图像识别 自适应提升 有效区域基因选择 多特征融合    
Abstract:To resolve the problems of insufficient medical resources and increase consultations, it is very necessary to design a computer-aided breast cancer image recognition model, which assists pathologists in their clinical diagnosis more effectively. However, existing works only use single feature to complete the recognition procedure. It means they didn't use the implicit complementarity among different features. To address the problem, an novel modified Adaboost algorithm is proposed. The traditional effective range based gene selection (ERGS) algorithm is modified to dynamically calculate the ERGS weight of each feature after several image features such as SIFT, Gist, HOG, and VGG16 are extracted. Then the Adaboost algorithm is utilized to build a strong classifier and the estimated probabilities are weighted by the corresponding ERGS weights to complete multi-feature fusion. Experimental results demonstrate as follows. 1)The accuracy of the proposed algorithm is about 86.24%, which is 3.82% higher than the most competitive baseline. 2)There is a strong complementarity among the SIFT, Gist and HOG features, which helps more accurately describe the breast cancer image. 3)Positive images are easier to be recognized by the proposed algorithm.
Key wordsbreast cancer image recognition    Adaboost    effective range based gene selection    multi-feature fusion
收稿日期: 2020-12-01     
引用本文:   
张红斌,邬任重,蒋子良,武晋鹏,袁 天,滑 瑾,姬东鸿. 基于改进的自适应提升算法的乳腺癌图像识别研究[J]. 华中师范大学学报(自然科学版), 2020, 54(6): 935-943.
ZHANG Hongbin,WU Renzhong,JIANG Ziliang,WU Jinpeng,YUAN Tian,HUA Jin,JI Donghong. Breast cancer image recognition based on modified Adaboost algorithm. journal1, 2020, 54(6): 935-943.
链接本文:  
https://journal.ccnu.edu.cn/zk/CN/     或     https://journal.ccnu.edu.cn/zk/CN/Y2020/V54/I6/935
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