Abstract
Glaucoma and Diabetic Retinopathy are widely prevalent diseased eye conditions which gradually lead to blindness. Early and timely diagnosis requires the help of expert ophthalmologists which is not available everywhere. As a result, many attempts have been made to come up with fully automated and intelligent systems to address this issue. A very important component of this task is detecting the optic disc. This work aims to establish a clear and concise picture of the present state-of-the-art models on this problem and benchmark their robustness and versatility to adapt to a variety of scans and images. This paper aims to deploy and review various deep learning architectures on a uniform test bed to establish the best models in optic disc detection. GAN approaches (pOSAL and CFEA) give the best performance, giving Dice Coefficient of 0.96 and 0.94. This is followed by specialized CNN architectures such as U-Net, M-Net and P-Net, giving Dice Coefficients between 0.93 and 0.86.
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Kumar, S., Raman, S. (2023). Benchmarking State-of-the-Art Methodologies for Optic Disc Segmentation. In: Thakur, M., Agnihotri, S., Rajpurohit, B.S., Pant, M., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Lecture Notes in Networks and Systems, vol 547. Springer, Singapore. https://doi.org/10.1007/978-981-19-6525-8_1
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