A Comprehensive Survey of Deep Learning and Ensemble Techniques in Glaucoma Detection
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Abstract
Glaucoma stands as a primary contributor to irreversible blindness, necessitating precise and prompt diagnoses for effective management. Recent progress in deep learning, particularly through the use of ensemble methods involving Convolutional Neural Networks (CNNs), has demonstrated considerable potential in automating the detection of glaucoma by analyzing ocular imaging data, such as fundus and Optical Coherence Tomography (OCT) images. This survey provides a thorough overview of the latest ensemble-based approaches developed for glaucoma detection, emphasizing the advantages of integrating various CNN architectures, including ResNet, VGG, and DenseNet, to enhance feature extraction and classification capabilities. The paper explores current trends in transfer learning, multi-modal data integration, and hybrid methodologies that reinforce the performance and adaptability of ensemble methods in clinical environments. Additionally, it addresses challenges like the necessity for high-quality labeled datasets, model interpretability, and generalization across different populations. By exploring into recent studies, the survey aims to identify limitations in existing systems and propose advancements in ensemble- based glaucoma detection, ultimately offering valuable insights into future research path that can narrow the gap between experimental findings and practical clinical applications.
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