According to the World Health Organization, glaucoma is a leading cause of blindness, accounting for over 12% of global blindness as it affects one in every 100 people. In fact, 79.6 million people worldwide live with blindness caused by glaucoma. This is because the current method for diagnosing glaucoma is by examining retinal fundus images. However, it is considerably difficult to distinguish the lesions' features solely through manual observations by ophthalmologists, especially in the early phases. This study proposes a new diagnosis method using convolutional neural networks. The attention mechanism is utilized to learn pixel-wise features for accurate prediction. Several attention strategies have been developed to guide the networks in learning the important features and factors that affect localization accuracy. The algorithms were trained for glaucoma detection using Python 2.7, TensorFlow, Py Torch, and Keras. The methods were evaluated on Drishti-GS and RIM-ONE datasets with 361 training and 225 test sets, consisting of 344 healthy and 242 glaucomatous images. The proposed algorithms can achieve impressive results that show an increase in overall diagnostic efficiency, as the algorithm displays a 30-second detection time with 98.9% accuracy compared to the 72.3% accuracy of traditional testing methods. Finally, this algorithm has been implemented as a webpage, allowing patients to test for glaucoma. This webpage offers various services such as: connecting the patient to the nearest care setup; offering scientific articles regarding glaucoma; and a video game that supports eye-treatment yogic exercises to strengthen vision and focus. This early diagnostic method has the near future potential to decrease the percentage of irreversible vision loss due to glaucoma by 42.79% (the percentage was calculated using the mean absolute error function), which could prevent glaucoma from remaining the leading cause of blindness worldwide.

The authors have declared no competing interest.

This study was funded by King Abdullah Medical City

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DRISHTI-GS Dataset: https://www.kaggle.com/datasets/lokeshsaipureddi/drishtigs-retina-dataset-for-onh-segmentation RIM-One Dataset: https://www.researchgate.net/publication/345850772_RIM-ONE_DL_A_Unified_Retinal_Image_Database_for_Assessing_Glaucoma_Using_Deep_Learning

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