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TRk-CNN: Transferable Ranking-CNN for image classification of glaucoma, glaucoma suspect, and normal eyes

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Abstract
In this paper, we proposed Transferable Ranking Convolutional Neural Network (TRk-CNN) that can be effectively
applied when the classes of images to be classified show a high correlation with each other. The multi-class
classification method based on the softmax function, which is generally used, is not effective in this case because
the inter-class relationship is ignored. Although there is a Ranking-CNN that takes into account the ordinal
classes, it cannot reflect the inter-class relationship to the final prediction. TRk-CNN, on the other hand, combines
the weights of the primitive classification model to reflect the inter-class information to the final classification
phase. We evaluated TRk-CNN in glaucoma image dataset that was labeled into three classes: normal,
glaucoma suspect, and glaucoma eyes. Based on the literature we surveyed, this study is the first to classify three
status of glaucoma fundus image dataset into three different classes. We compared the evaluation results of TRk-
CNN with Ranking-CNN (Rk-CNN) and multi-class CNN (MC?CNN) using the DenseNet as the backbone CNN
model. As a result, TRk-CNN achieved an average accuracy of 92.96%, specificity of 93.33%, sensitivity for
glaucoma suspect of 95.12% and sensitivity for glaucoma of 93.98%. Based on average accuracy, TRk-CNN is
8.04% and 9.54% higher than Rk-CNN and MC?CNN and surprisingly 26.83% higher for sensitivity for suspicious
than multi-class CNN. Our TRk-CNN is expected to be effectively applied to the medical image classification
problem where the disease state is continuous and increases in the positive class direction.
Author(s)
김대영김도현김영학김채리박지혜엄영섭전태준Hoang Minh Nguyen
Issued Date
2021
Type
Article
Keyword
GlaucomaGlaucoma suspectConvolutional neural networksRanking classification
DOI
10.1016/j.eswa.2021.115211
URI
https://oak.ulsan.ac.kr/handle/2021.oak/8275
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_gale_infotracacademiconefile_A673106679&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,TRk-CNN:%20Transferable%20Ranking-CNN%20for%20image%20classification%20of%20glaucoma,%20glaucoma%20suspect,%20and%20normal%20eyes&offset=0&pcAvailability=true
Publisher
EXPERT SYSTEMS WITH APPLICATIONS
Location
영국
Language
영어
ISSN
0957-4174
Citation Volume
182
Citation Start Page
115211
Citation End Page
115211
Appears in Collections:
Medicine > Medicine
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