Fundus Image Classification via an Integrated Deep Learning Model and Random Forest for Glaucoma Diagnostics

Haotian Zeng, Jiheng Cong, Hantong Hong, Zihan Yu, Fan Zhang*, Guojie Li

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

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Abstract

Glaucoma is a leading cause of irreversible blindness, often exacerbated by delayed diagnosis. Traditional diagnostic methods have limitations in early detection and require significant expertise. Recent advancements in Machine Learning and Deep Learning have shown promise in enhancing the diagnostic accuracy and efficiency for glaucoma using fundus imagery. This study presents a four-stage method for glaucoma classification. Fundus images are processed using VGG16 and ViT for feature extraction, capturing both local and global features. These features are then fused and reduced in dimensionality using Principal Component Analysis. Finally, the reduced features are classified using a Random Forest classifier. The integrated feature fusion model demonstrates significant improvements in diagnostic performance, achieving higher accuracy, specificity, and sensitivity in distinguishing between non-referable glaucoma and referable glaucoma classes compared to traditional methods. Specifically, our model achieved an accuracy of 94.2%, an F1 score of 94.2%, a sensitivity of 94.44%, and a specificity of 94.0%. The use of both CNNs and ViTs for feature extraction leverages their strengths, resulting in a more effective diagnostic tool. The combination of CNNs, ViTs, and Random Forest classifiers, along with advanced data augmentation techniques, shows substantial potential for early glaucoma detection and ongoing monitoring. This approach addresses the limitations of current ML models and enhances diagnostic accuracy and efficiency, making it a promising tool for clinical settings.
Original languageEnglish
Publication statusAccepted/In press - 22 Aug 2024
EventInternational Conference on Intelligent Manufacturing and Robotics 2024 - Taicang, Suzhou, China
Duration: 22 Aug 202423 Aug 2024
https://www.xjtlu.edu.cn/en/study/departments/school-of-intelligent-manufacturing-ecosystem/icimr2024

Conference

ConferenceInternational Conference on Intelligent Manufacturing and Robotics 2024
Abbreviated titleICiMR 2024
Country/TerritoryChina
CityTaicang, Suzhou
Period22/08/2423/08/24
Internet address

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