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Purpose: Glaucoma is one of the preeminent causes of incurable visual disability and blindness across the world due to elevated intraocular pressure within the eyes. Accurate and timely diagnosis is essential for preventing visual disability. Manual detection of glaucoma is a challenging task that needs expertise and years of experience. Methods: In this paper, we suggest a powerful and accurate algorithm using a convolutional neural network (CNN) for the automatic diagnosis of glaucoma. In this work, 1113 fundus images consisting of 660 normal and 453 glaucomatous images from four databases have been used for the diagnosis of glaucoma. A 13-layer CNN is potently trained from this dataset to mine vital features, and these features are classified into either glaucomatous or normal class during testing. The proposed algorithm is implemented in Google Colab, which made the task straightforward without spending hours installing the environment and supporting libraries. To evaluate the effectiveness of our algorithm, the dataset is divided into 70% for training, 20% for validation, and the remaining 10% utilized for testing. The training images are augmented to 12012 fundus images. Results: Our model with SoftMax classifier achieved an accuracy of 93.86%, sensitivity of 85.42%, specificity of 100%, and precision of 100%. In contrast, the model with the SVM classifier achieved accuracy, sensitivity, specificity, and precision of 95.61, 89.58, 100, and 100%, respectively. Conclusion: These results demonstrate the ability of the deep learning model to identify glaucoma from fundus images and suggest that the proposed system can help ophthalmologists in a fast, accurate, and reliable diagnosis of glaucoma.

Glaucoma is an optic neuropathy characterized by progressive degeneration of retinal ganglion cells. The early identification of Glaucoma is extremely important as it is detrimental to one’s blindness. In this paper, we present the identification of glaucoma using faster R-CNN which is one of the most well-known object detection neural networks. The proposed method uses artificial intelligence and enhanced deep learning to detect Glaucoma. Faster R-CNN comprises two modules, the region proposal network (RPN), in which the region of object is distinguished on the picture, and a network that enables to classify the objects in the proposed region. We have accomplished the finest output by applying a transfer learning scheme with ResNet50 and VGG16. Using ResNet we have detected Glaucoma with up to 96% accuracy. The test results obtained by making use of two unique publicly available data sets DRISHTI_GS and ORIGA with 751 images demonstrate that this arrangement can be a significant alternative for the computer design aid framework for the large-scale screening programs of glaucoma detection.