Performance and Evolution of Glaucoma Detection in Retinal Fundus Images Using Various Deep Learning Architectures
Keywords:
ENet, ResNet50, GoogLeNet, inceptionv3, Optic Cup (OC) and Optic Disc (OD)Abstract
The leading cause of irreversible eyesight loss worldwide is glaucoma. Early identification of glaucoma is difficult because symptoms don't appear until the disease is progressed. Routine glaucoma screening is recommended. The eye screening process now requires subjective assessments, which take time and energy. Ophthalmologists are few. Ophthalmologists use a two-step computerized glaucoma screening to reduce effort. This study analyzes advanced deep learning methods including ENet, ResNet50, GoogLeNet, inceptionv3, and AlexNet for glaucoma diagnosis. Glaucoma categorization and optic disc and cup segmentation are done using a pre-trained CNN. This approach calculates Cup-to-Disc Ratio more accurately. Compared to other designs, ENet is most accurate. Efficient Neural Networks (a subset of CNNs) optimize finite resources through lightweight and efficient inference. Training and testing the ENet architecture on a public Kaggle dataset yields 98.40% segmentation accuracy.
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