Glaucoma Detection using Machine Learning with Fundus Images

Abstract

Glaucoma is a leading global cause of the irreversible blindness, presents significant challenges due to its asymptomatic nature in the early stages, which often delays diagnosis until substantial vision loss has occurred. Addressing this critical health issue, the study develops and evaluates an automated glaucoma detection system leveraging the EfficientNetV2 convolutional neural network (CNN) architecture. By utilizing fundus images, a non-invasive diagnostic tool, the system identifies essential structural indicators of the glaucoma, such as optic nerve cupping and retinal nerve fiber layer (RNFL) thinning. The methodology follows a three-phase approach: image preprocessing for normalization and resizing to ensure data consistency, feature extraction using a fine-tuned EfficientNetV2 model to detect glaucomatous changes, and classification into glaucomatous and non-glaucomatous categories. To further enhance detection accuracy, feature selection techniques such as cup-to-disc ratio (CDR) calculation and RNFL thickness measurement were integrated into the process. Experimental results demonstrate the model's exceptional performance, achieving a 93.7% overall accuracy, with a precision of 93.5%, recall of 92.5% for glaucomatous cases, reflecting its ability to identify true positives effectively. Comparative analysis against other architectures, including Inception-ResNet-V2 and traditional CNNs, confirmed EfficientNetV2's superior accuracy, precision, and computational efficiency. Metrics such as precision, recall, and the confusion matrix validated the model's reliability and scalability in real-world diagnostic scenarios. This study underscores the potential of advanced machine learning techniques in revolutionizing glaucoma detection, enabling early intervention to prevent irreversible vision loss. Despite challenges such as dependency on high-quality imaging, false classifications, and the need for regular updates to accommodate diverse populations and advancements in imaging technologies, the system offers a scalable, cost-effective solution. It supports the standardization of diagnostic protocols, facilitates interdisciplinary collaboration, and emphasizes the importance of integrating automated tools in clinical practices. This approach represents a significant step forward in addressing the global burden of glaucoma and improving patient outcomes.