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This is a Quantitative Study study.

October 11, 2025Open Access

Explainable Deep Learning for Glaucoma Detection: A DenseNet121-Based Classification with Grad-CAM Visualization

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The model achieved a validation accuracy of 90.16% on the ACRIMA dataset, showcasing its effectiveness in glaucoma classification.
Using techniques like Grad-CAM, the study highlighted important decision-making areas in fundus images for better clinical interpretability.
Data augmentation and class balancing were crucial in addressing dataset imbalances, enhancing the model's performance.
Future developments aim to integrate multimodal imaging and advanced explanation frameworks to improve clinical reliability.

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This analysis finds a 90.16% accuracy in glaucoma detection using deep learning, indicating significant promise for automated screening.

Pathmakumara et al. (2025) studied this question.

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