Severity Classification of Conjunctival Hyperaemia by Deep Neural Network Ensembles
Joint Authors
Masumoto, Hiroki
Tabuchi, Hitoshi
Yoneda, Tsuyoshi
Nakakura, Shunsuke
Ohsugi, Hideharu
Sumi, Tamaki
Fukushima, A.
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-06-02
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Conjunctival hyperaemia is a common clinical ophthalmological finding and can be a symptom of various ocular disorders.
Although several severity classification criteria have been proposed, none include objective severity criteria.
Neural networks and deep learning have been utilised in ophthalmology, but not for the purpose of classifying the severity of conjunctival hyperaemia objectively.
To develop a conjunctival hyperaemia grading software, we used 3700 images as the training data and 923 images as the validation test data.
We trained the nine neural network models and validated the performance of these networks.
We finally chose the best combination of these networks.
The DenseNet201 model was the best individual model.
The combination of the DenseNet201, DenseNet121, VGG19, and ResNet50 were the best model.
The correlation between the multimodel responses, and the vessel-area occupied was 0.737 (p<0.01).
This system could be as accurate and comprehensive as specialists but would be significantly faster and consistent with objective values.
American Psychological Association (APA)
Masumoto, Hiroki& Tabuchi, Hitoshi& Yoneda, Tsuyoshi& Nakakura, Shunsuke& Ohsugi, Hideharu& Sumi, Tamaki…[et al.]. 2019. Severity Classification of Conjunctival Hyperaemia by Deep Neural Network Ensembles. Journal of Ophthalmology،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1186221
Modern Language Association (MLA)
Masumoto, Hiroki…[et al.]. Severity Classification of Conjunctival Hyperaemia by Deep Neural Network Ensembles. Journal of Ophthalmology No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1186221
American Medical Association (AMA)
Masumoto, Hiroki& Tabuchi, Hitoshi& Yoneda, Tsuyoshi& Nakakura, Shunsuke& Ohsugi, Hideharu& Sumi, Tamaki…[et al.]. Severity Classification of Conjunctival Hyperaemia by Deep Neural Network Ensembles. Journal of Ophthalmology. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1186221
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1186221