Abstract: Glaucoma is a term used to describe the cumulative loss of retinal cells in the optic nerve or permanent vision loss due to optic neuropathy. Glaucoma is a disease of the human eye. This disease is considered an irreversible disease that causes deterioration of vision. They have no early warning signs of glaucoma. You may not notice a change in your vision because the effect is so subtle. Many deep learning (DL) models have been developed to improve the diagnosis of glaucoma. Therefore, we present an architecture for accurate glaucoma detection based on deep learning using convolutional neural networks (CNN). The distinction between glaucoma and non-glaucoma patterns can be made using CNN. CNN provides a hierarchical structure for image differentiation. Using the current method, the disease is detected. It determines whether the patient has glaucoma or not, the relationship between the eye and the disc. Improved diagnosis by combining image data generator techniques to augment data. The results show that the proposed model outperforms existing algorithms, achieving 98.47% accuracy.
Keywords: Feature Extraction, Machine Learning, CNN, Image Data Generator,Glaucoma,keras,streamlit
Cite:
Akash Ashok Nayak, A Ashitha, Akshatha, Alan Raji Mani, Dr.Ravinarayana B, Mr.Shreejith K B, "Detection Of Glaucoma Eye Disease Using Retinal Fundus Images", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.133102.
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DOI:
10.17148/IJARCCE.2024.133102
