Deep Learning-Based Image Analysis for Glaucoma Diagnosis
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Abstract
This research aims to develop a Convolutional Neural Network (CNN) model for the automatic classification of glaucoma from retinal fundus images. The dataset consisted of 1,000 eye images, divided into normal and glaucoma groups, collected from online databases and patients at Rajthanee Hospital, Rojana, Phra Nakhon Si Ayutthaya Province.The proposed model is a 5-layer CNN architecture designed to suit color image data. The data preprocessing steps included image resizing, grayscale conversion, and data augmentation to increase the diversity of the training dataset. Model performance was evaluated using 5-fold cross-validation on a training and validation set of 800 images. The results showed that the model achieved an accuracy of 87% during training. When tested with a new dataset of 200 images, the model demonstrated high performance, achieving an accuracy of 94.0%, precision of 93.1%, recall of 95.0%, F1-score of 94.0%, and an AUC of 0.98. These results indicate the model’s strong accuracy and balance in distinguishing between normal and glaucoma images, suggesting that the developed CNN model can be effectively applied in real-world scenarios, particularly in healthcare facilities with limited resources and specialized personnel.
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