Screening of Common Retinal Diseases Using Six-Category Models Based on EfficientNet

Shaojun Zhu; Bing Lu; Chenghu Wang; Maonian Wu; Bo Zheng; Qin Jiang; Ruili Wei; Qixin Cao; Weihua Yang

doi:10.3389/fmed.2022.808402

Screening of Common Retinal Diseases Using Six-Category Models Based on EfficientNet

Front Med (Lausanne). 2022 Feb 23:9:808402. doi: 10.3389/fmed.2022.808402. eCollection 2022.

Authors

Shaojun Zhu^{1

2}, Bing Lu¹, Chenghu Wang³, Maonian Wu^{1

2}, Bo Zheng^{1

2}, Qin Jiang³, Ruili Wei⁴, Qixin Cao⁵, Weihua Yang³

Affiliations

¹ School of Information Engineering, Huzhou University, Huzhou, China.
² Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou, China.
³ The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China.
⁴ Department of Ophthalmology, Shanghai Changzheng Hospital, Huangpu, China.
⁵ Huzhou Traditional Chinese Medicine Hospital Affiliated to Zhejiang University of Traditional Chinese Medicine, Huzhou, China.

Abstract

Purpose: A six-category model of common retinal diseases is proposed to help primary medical institutions in the preliminary screening of the five common retinal diseases.

Methods: A total of 2,400 fundus images of normal and five common retinal diseases were provided by a cooperative hospital. Two six-category deep learning models of common retinal diseases based on the EfficientNet-B4 and ResNet50 models were trained. The results from the six-category models in this study and the results from a five-category model in our previous study based on ResNet50 were compared. A total of 1,315 fundus images were used to test the models, the clinical diagnosis results and the diagnosis results of the two six-category models were compared. The main evaluation indicators were sensitivity, specificity, F1-score, area under the curve (AUC), 95% confidence interval, kappa and accuracy, and the receiver operator characteristic curves of the two six-category models were compared in the study.

Results: The diagnostic accuracy rate of EfficientNet-B4 model was 95.59%, the kappa value was 94.61%, and there was high diagnostic consistency. The AUC of the normal diagnosis and the five retinal diseases were all above 0.95. The sensitivity, specificity, and F1-score for the diagnosis of normal fundus images were 100, 99.9, and 99.83%, respectively. The specificity and F1-score for RVO diagnosis were 95.68, 98.61, and 93.09%, respectively. The sensitivity, specificity, and F1-score for high myopia diagnosis were 96.1, 99.6, and 97.37%, respectively. The sensitivity, specificity, and F1-score for glaucoma diagnosis were 97.62, 99.07, and 94.62%, respectively. The sensitivity, specificity, and F1-score for DR diagnosis were 90.76, 99.16, and 93.3%, respectively. The sensitivity, specificity, and F1-score for MD diagnosis were 92.27, 98.5, and 91.51%, respectively.

Conclusion: The EfficientNet-B4 model was used to design a six-category model of common retinal diseases. It can be used to diagnose the normal fundus and five common retinal diseases based on fundus images. It can help primary doctors in the screening for common retinal diseases, and give suitable suggestions and recommendations. Timely referral can improve the efficiency of diagnosis of eye diseases in rural areas and avoid delaying treatment.

Keywords: computer simulation; fundus; optical imaging; retinal diseases; vision screening.