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Automatic Kidney Lesion Detection for CT Images Using Morphological Cascade Convolutional Neural Networks

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成果类型:
期刊论文
作者:
Zhang, Hui*;Chen, Yurong*;Song, Yanan;Xiong, Zhenlin;Yang, Yimin;...
通讯作者:
Zhang, Hui;Chen, Yurong
作者机构:
[Zhang, Hui; Song, Yanan; Xiong, Zhenlin; Chen, Yurong; Zhang, H] Changsha Univ Sci & Technol, Dept Elect & Informat Engn, Changsha 410114, Hunan, Peoples R China.
[Yang, Yimin] Lakehead Univ, Comp Sci Dept, Thunder Bay Campus, Thunder Bay, ON P7B 5E1, Canada.
[Wu, Q. M. Jonathan] Univ Windsor, Dept Elect & Comp Engn, Windsor, ON N9B 3P4, Canada.
通讯机构:
[Zhang, H; Chen, YR] C
Changsha Univ Sci & Technol, Dept Elect & Informat Engn, Changsha 410114, Hunan, Peoples R China.
语种:
英文
关键词:
deep learning;Kidney lesion detect;morphology;RCNN
期刊:
IEEE ACCESS
ISSN:
2169-3536
年:
2019
卷:
7
页码:
83001-83011
基金类别:
This work was supported in part by the National Natural Science Foundation of China under Grant 61601061, Grant 61841103, and Grant 81401490, in part by the National Key Research and Development Program of China under Grant 2018YFB1308200, in part by the Scientific Research Fund of the Hunan Provincial Education Department under Grant 17C0046, in part by the Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing under Grant IRT2018009, and in part by the Hunan Key Project of Research and Development Plan under Grant 2018GK2022 and Grant 2018JJ3079.
机构署名:
本校为第一且通讯机构
院系归属:
电气与信息工程学院
摘要:
The CT scan image is one of the most useful tools for diagnosing and locating lesions in the kidney. It can provide precise information about the location and size of lesions in many medical applications. Manual and traditional medical testings are labor-consuming and time-costing. Nowadays, detecting lesions in CT automatically is an integral assignment to the paramount importance of clinical diagnosis. Computer-aided diagnosis (CAD) is needed to develop and improve medical testing efficiency. However, it is still a tremendous challenge to the...

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