Multi-stage glaucoma classification using pre-trained convolutional neural networks and voting-based classifier fusion - Velpula, Vijaya Kumar; Sharma, Lakhan Dev (Frontiers in Physiology, 2023)
Aim: To design an automated glaucoma detection system for early detection of glaucoma using fundus images. Background: Glaucoma is a serious eye problem that can cause vision loss and even permanent blindness. Early detection and prevention are crucial for effective treatment. Traditional diagnostic approaches are time-consuming, manual, and often inaccurate, making automated glaucoma diagnosis necessary. Objective: To propose an automated glaucoma stage classification model using pre-trained deep convolutional neural network (CNN) models and classifier fusion. Methods: The proposed model utilized five pre-trained CNN models: Resnet50, Alexnet, VGG19, Densenet-201, and InceptionResnet-v2. The model was tested using four public datasets: ACRIMA, RIM-ONE, Harvard Dataverse, and Drishti. Classifier fusion was created to merge the decisions of all CNN models using the maximum voting-based approach. Results: The proposed model achieved an area under the curve (AUC) of 1 and an accuracy of 99.57% for the ACRIMA dataset. The HVD dataset had an AUC of 0.97 and an accuracy of 85.43%. The accuracy rates for Drishti and RIMONE were 90.55% and 94.95%, respectively. The experimental results showed that the proposed model performed better than state-of-the-art methods in classifying glaucoma in its early stages. Conclusion: The proposed automated glaucoma stage classification model using pre-trained CNN models and classifier fusion is an effective method for the early detection of glaucoma. The results indicate high accuracy rates and superior performance compared to existing methods