Abstract
Glaucoma is one of the ocular diseases which is progressive and irreversible disease. Early detection of glaucoma helps in saving the vision loss permanently. In India, the ophthalmologists are limited in numbers to check the patient. Owing to this, automated detection of glaucoma from the fundus images of the eye region is the state of the art in medical imaging. In this work, the most common method for detection of glaucoma is a parameter called the cup-to-disc ratio is done with deep learning technology. The optic disc and optic cup segmentation are employed with the proposed MultiResUNet architecture which is a fusion of Res and MultiRes paths to U-Net architecture. The areas of the optic disc and optic cup are computed using the segmented images, and the cup-to-disc ratio (CDR) value is further calculated using the areas. The segmentation result from the MultiResUNet segmentation is compared to the standard U-Net segmentation with the Jaccard Index performance metric. The empirical results indicate that MultiResUNet obtained a mean classification accuracy of 97.2% an increase of 7.4% compared to U-Net segmentation.
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Roshini, R., Alex, J.S.R. (2022). Automatic Segmentation of Optic Cup and Optic Disc Using MultiResUNet for Glaucoma Classification from Fundus Image. In: Saraswat, M., Sharma, H., Arya, K.V. (eds) Intelligent Vision in Healthcare. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-16-7771-7_4
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