硕士生导师
招生学科专业:
信息与通信工程 -- 【招收硕士研究生】 -- 电子信息工程学院
电子信息 -- 【招收硕士研究生】 -- 电子信息工程学院
性别:女
毕业院校:名古屋工业大学
学历:日本名古屋工业大学
学位:工学博士学位
所在单位:电子信息工程学院
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所属单位:电子信息工程学院
发表刊物:Lect. Notes Comput. Sci.
摘要:Retinal image quality classification makes a great difference in automated diabetic retinopathy screening systems. With the increase of application of portable fundus cameras, we can get a large number of retinal images, but there are quite a number of images in poor quality because of uneven illumination, occlusion and patients movements. Using the dataset with poor quality training networks for DR screening system will lead to the decrease of accuracy. In this paper, we first explore four CNN architectures (AlexNet, GoogLeNet, VGG-16, and ResNet-50) from ImageNet image classification task to our Retinal fundus images quality classification, then we pick top two networks out and jointly fine-tune the two networks. The total loss of the network we proposed is equal to the sum of the losses of all channels. We demonstrate the super performance of our proposed algorithm on a large retinal fundus image dataset and achieve an optimal accuracy of 97.12%, outperforming the current methods in this area. © Springer International Publishing AG 2017.
ISSN号:0302-9743
是否译文:否
发表时间:2017-01-01
合写作者:孙晶,Cheng, Jun,Yu, Fengli,Liu, Jiang
通讯作者:万程