상세정보
학술지
Deep Learning Ensemble Method for Classifying Glaucoma Stages Using Fundus Photographs and Convolutional Neural Networks
- 발행일
- 202110
- 출처
- Current Eye Research, v.46 no.10, pp.1-10
- ISSN
- 0271-3683
- 출판사
- Taylor & Francis
- 협약과제
- 17HS2400, 다중소스 데이터 지능형 분석기반 고수준 정보추출 원천기술 연구, 유장희
- 초록
- Purpose: This study developed and evaluated a deep learning ensemble method to automatically grade the stages of glaucoma depending on its severity. Materials and Methods: After cross-validation of three glaucoma specialists, the final dataset comprised of 3,460 fundus photographs taken from 2,204 patients were divided into three classes: unaffected controls, early-stage glaucoma, and late-stage glaucoma. The mean deviation value of standard automated perimetry was used to classify the glaucoma cases. We modeled 56 convolutional neural networks (CNN) with different characteristics and developed an ensemble system to derive the best performance by combining several modeling results. Results: The proposed method with an accuracy of 88.1% and an average area under the receiver operating characteristic of 0.975 demonstrates significantly better performance to classify glaucoma stages compared to the best single CNN model that has an accuracy of 85.2% and an average area under the receiver operating characteristic of 0.950. The false negative is the least adjacent misprediction, and it is less in the proposed method than in the best single CNN model. Conclusions: The method of averaging multiple CNN models can better classify glaucoma stages by using fundus photographs than a single CNN model. The ensemble method would be useful as a clinical decision support system in glaucoma screening for primary care because it provides high and stable performance with a relatively small amount of data.
- KSP 제안 키워드
- Best performance, CNN model, Clinical Decision Support System(CDSS), Convolution neural network(CNN), Cross validation(CV), Decision Support System(DSS), Deviation value, Early stages, Ensemble System, Ensemble method, False negative
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