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Deep Learning for Glaucoma Detection: R-CNN ResNet-50 and Image Segmentation

Abstract:

Glaucoma is a leading cause of irreversible blindness worldwide, affecting millions of people. Early diagnosis is essential to reduce visual loss, and various techniques are used for glaucoma detection. In this work, a hybrid method for glaucoma fundus image localization using pre-trained Region-based Convolutional Neural Networks (R-CNN) ResNet-50 and cup-to-disk area segmentation is proposed. The ACRIMA and ORIGA databases were used to evaluate the proposed approach. The results showed an average confidence of 0.879 for the ResNet-50 model, indicating it as a reliable alternative for glaucoma detection. Moreover, the cup-to-disc ratio was calculated using Gradient-color-based optic disc segmentation, coinciding with the ResNet-50 results in 80% of cases, having an average confidence score of 0.84. The approach suggested in this study can determine if glaucoma is present or not, with a final accuracy of 95% with specific criteria provided to guide the specialist for an accurate diagnosis. In summary, the proposed model provides a reliable and secure method for diagnosing glaucoma using fundus images.