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Abstract #82864 Published in IGR 20-4
Automated Glaucoma Screening from Retinal Fundus Image Using Deep Learning
Phasuk S; Tantibundhit C; Poopresert P; Yaemsuk A; Suvannachart P; Itthipanichpong R; Chansangpetch S; Manassakorn A; Tantisevi V; Rojanapongpun PConference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 904-907
Glaucoma is the second leading cause of blindness worldwide. This paper proposes an automated glaucoma screening method using retinal fundus images via the ensemble technique to fuse the results of different classification networks and the result of each classification network was fed as an input to a simple artificial neural network (ANN) to obtain the final result. Three public datasets, i.e., ORIGA-650, RIM-ONE R3, and DRISHTI-GS were used for training and evaluating the performance of the proposed network. The experimental results showed that the proposed network outperformed other state-of-art glaucoma screening algorithms with AUC of 0.94. Our proposed algorithms showed promising potential as a medical support system for glaucoma screening especially in low resource countries.
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Classification:
1.6 Prevention and screening (Part of: 1 General aspects)
6.9.2.2 Posterior (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis > 6.9.2 Optical coherence tomography)
6.9.5 Other (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis)
6.30 Other (Part of: 6 Clinical examination methods)
