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BMC Medical Informatics and Decision Making

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Open Access 01-12-2024 | Glaucoma | Research

A hybrid framework for glaucoma detection through federated machine learning and deep learning models

Authors: Abeer Aljohani, Rua Y. Aburasain

Published in: BMC Medical Informatics and Decision Making | Issue 1/2024

Abstract

Background

Glaucoma, the second leading cause of global blindness, demands timely detection due to its asymptomatic progression. This paper introduces an advanced computerized system, integrates Machine Learning (ML), convolutional neural networks (CNNs), and image processing for accurate glaucoma detection using medical imaging data, surpassing prior research efforts.

Method

Developing a hybrid glaucoma detection framework using CNNs (ResNet50, VGG-16) and Random Forest. Models analyze pre-processed retinal images independently, and post-processing rules combine predictions for an overall glaucoma impact assessment.

Result

The hybrid framework achieves a significant 95.41% accuracy, with precision and recall at 99.37% and 88.37%, respectively. The F1 score, balancing precision and recall, reaches a commendable 93.52%. These results highlight the robustness and effectiveness of the hybrid framework in accurate glaucoma diagnosis.

Conclusion

In summary, our research presents an innovative hybrid framework combining CNNs and traditional ML models for glaucoma detection. Using ResNet50, VGG-16, and Random Forest in an ensemble approach yields remarkable accuracy, precision, recall, and F1 score. These results showcase the methodology’s potential to enhance glaucoma diagnosis, emphasizing its promising role in early detection and preventing irreversible vision loss. The integration of ML and DNNs in medical imaging analysis suggests a valuable path for future advancements in ophthalmic healthcare.

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Title: A hybrid framework for glaucoma detection through federated machine learning and deep learning models
Authors: Abeer Aljohani
Rua Y. Aburasain
Publication date: 01-12-2024
Publisher: BioMed Central
Keyword: Glaucoma
Published in: BMC Medical Informatics and Decision Making / Issue 1/2024
Electronic ISSN: 1472-6947
DOI: https://doi.org/10.1186/s12911-024-02518-y

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A hybrid framework for glaucoma detection through federated machine learning and deep learning models

Abstract

Background

Method

Result

Conclusion

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Springer Medicine

Abstract

Background

Method

Result

Conclusion

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