Thisara Shyamalee, Dulani Meedeniya. Glaucoma Detection with Retinal Fundus Images Using Segmentation and Classification. Machine Intelligence Research, vol. 19, no. 6, pp.563-580, 2022. https://doi.org/10.1007/s11633-022-1354-z
Citation: Thisara Shyamalee, Dulani Meedeniya. Glaucoma Detection with Retinal Fundus Images Using Segmentation and Classification. Machine Intelligence Research, vol. 19, no. 6, pp.563-580, 2022. https://doi.org/10.1007/s11633-022-1354-z

Glaucoma Detection with Retinal Fundus Images Using Segmentation and Classification

doi: 10.1007/s11633-022-1354-z
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  • Author Bio:

    Thisara Shyamalee received the B. Sc. degree in information technology from Sri Lanka Institute of Information Technology (SLIIT), Sri Lanka in 2019. She is currently a master student in computer science (with a major component of research) at Department of Computer Science and Engineering, University of Moratuwa, Sri Lanka. Her research interests include machine learning, deep learning, and computer interaction. E-mail: thisaras@cse.mrt.ac.lk ORCID iD: 0000-0001-7533-779X

    Dulani Meedeniya received the Ph. D. degree in computer science from University of St Andrews, UK in 2013. She is a professor in computer science and engineering at the University of Moratuwa, Sri Lanka. She is the director of the Bio-Health Informatics Group in her department and engages in many collaborative projects. She is a co-author of 100+ publications in indexed journals, peer-reviewed conferences and international book chapters. She has received several awards and grants for her contribution in research. She serves as a reviewer, program committee and editorial team member in many international conferences and journals. She is a fellow of HEA (UK), MIET, senior member IEEE, member ACM and a chartered engineer registered at EC (UK). Her research interests include software modelling and design, bio-health informatics, deep learning and technology-enhanced learning. E-mail: dulanim@cse.mrt.ac.lk (Corresponding author) ORCID iD: 0000-0002-4520-3819

  • Received Date: 2022-04-05
  • Accepted Date: 2022-06-21
  • Publish Online: 2022-11-07
  • Publish Date: 2022-11-22
  • Glaucoma is a prevalent cause of blindness worldwide. If not treated promptly, it can cause vision and quality of life to deteriorate. According to statistics, glaucoma affects approximately 65 million individuals globally. Fundus image segmentation depends on the optic disc (OD) and optic cup (OC). This paper proposes a computational model to segment and classify retinal fundus images for glaucoma detection. Different data augmentation techniques were applied to prevent overfitting while employing several data pre-processing approaches to improve the image quality and achieve high accuracy. The segmentation models are based on an attention U-Net with three separate convolutional neural networks (CNNs) backbones: Inception-v3, visual geometry group 19 (VGG19), and residual neural network 50 (ResNet50). The classification models also employ a modified version of the above three CNN architectures. Using the RIM-ONE dataset, the attention U-Net with the ResNet50 model as the encoder backbone, achieved the best accuracy of 99.58% in segmenting OD. The Inception-v3 model had the highest accuracy of 98.79% for glaucoma classification among the evaluated segmentation, followed by the modified classification architectures.

     

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