Detection of Glaucoma using Deep Learning
Keywords:area under curve, digital fundus image, region of interest, optic disc, cup disc ratio
Glaucoma is an incurable disease that impairs vision and livability. We used a convolutional neural network to construct a deep learning framework for the identification of spontaneous eye problem in this study. In-depth instructional strategies, like as convolutional neural networks, might evaluate visual sequences in which glaucoma as well as non-glaucoma characteristics may be segregated for testing objectives. The suggested Deep Learning architecture comprise of six study layers: four conv layers and two completely functional levels. Discontinuation and augmentation methods are utilized to increase the accuracy of glaucoma diagnosis. ORIGA and SCES details have been thoroughly tested.
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Copyright (c) 2022 Ravi Kumar Gupta, Utkarsh Sharma, Vivek Singh
This work is licensed under a Creative Commons Attribution 4.0 International License.