• LIKHITHA M
• AYESHA SIDDIQA K
• DEEPIKA M D
• MANVITHA M
• Prof Dr.Harish B G , PROF.CHETHAN KUMAR G S

Glaucoma is a chronic and irreversible eye disease, which leads to deterioration in vision and quality of life. In this paper, we develop a deep learning (DL) architecture with convolutional neural network for automated glaucoma diagnosis. Deep learning systems, such as convolutional neural networks (CNNs), can infer a hierarchical representation of images to discriminate between glaucoma and non-glaucoma patterns for diagnostic decisions. The proposed DL architecture contains six learned layers: four convolutional layers and two fully-connected layers. Dropout and data augmentation strategies are adopted to further boost the performance of glaucoma diagnosis. Extensive experiments are performed on the ORIGA and SCES datasets. The results show area under curve (AUC) of the receiver operating characteristic curve in glaucoma detection at 0.831 and 0.887 in the two databases, much better than state-of-the-art algorithms. The method could be used for glaucoma detection.

ML (Machine Learning), YOLO (You Only Look Once), OCR (Optical Character Recognition).

" GLAUCOMA DETECTION BASED ON DEEP CONVOLUTIONAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.h238-h243, July-2023, Available :http://www.jetir.org/papers/JETIR2307728.pdf

" GLAUCOMA DETECTION BASED ON DEEP CONVOLUTIONAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. pph238-h243, July-2023, Available at : http://www.jetir.org/papers/JETIR2307728.pdf