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GC-NET for classification of glaucoma in the retinal fundus image

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Abstract

Glaucoma is the second-most dominant cause for irreversible blindness, resulting in damage to the optic nerve. Ophthalmologist diagnoses this disease using a retinal examination of the dilated pupil. Since the diagnosis is a manual and laborious procedure, an automated approach for faster diagnosis is desirable. Convolutional neural networks (CNN) could allow automation of the diagnosis procedure due to their self-learning capabilities. This paper presents a deep learning-based glaucoma classification network (GC-NET) for classifying a retinal image as glaucomatous or non-glaucomatous. The proposed GC-NET has been tested on RIM-One and Drishti datasets. Our experimental results showed that GC-NET achieves accuracy of 97.51%, sensitivity of 98.78% and specificity of 96.20% with 0.81 true positive (Tp), 0.03 false positive (Fp), 0.76 true negative (Tn) and 0.01 false negative (Fn) which outperforms state of the art. Thus, the proposed approach could be very useful for initial screening of glaucoma patients.

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Acknowledgements

The authors are grateful to the Ministry of Human Resource Development (MHRD), Govt. of India for funding this project (17-11/2015-PN-1) under Design Innovation Centre (DIC) sub-theme Medical Devices and Restorative Technologies.

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  1. University Institute of Engineering and Technology, Panjab University, Chandigarh, India

    Mamta Juneja, Niharika Thakur, Sarthak Thakur, Archit Uniyal, Anuj Wani & Prashant Jindal

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Juneja, M., Thakur, N., Thakur, S. et al. GC-NET for classification of glaucoma in the retinal fundus image. Machine Vision and Applications 31, 38 (2020). https://doi.org/10.1007/s00138-020-01091-4

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