Real-Time Glaucoma Detection from Digital Fundus Images Using Self-ONNs

Devecioglu O. C., Malik J., Ince T., Kiranyaz S., ATALAY E., Gabbouj M.

IEEE Access, vol.9, pp.140031-140041, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 9
  • Publication Date: 2021
  • Doi Number: 10.1109/access.2021.3118102
  • Journal Name: IEEE Access
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Page Numbers: pp.140031-140041
  • Keywords: Neurons, Optical imaging, Feature extraction, Biomedical optical imaging, Image segmentation, Biological system modeling, Computational modeling, Convolutional neural networks, glaucoma detection, medical image processing, operational neural networks, transfer learning, OPEN-ANGLE GLAUCOMA, NEURONAL DIVERSITY, PREVALENCE, DIAGNOSIS, NETWORK
  • Eskisehir Osmangazi University Affiliated: Yes


© 2013 IEEE.Glaucoma leads to permanent vision disability by damaging the optical nerve that transmits visual images to the brain. The fact that glaucoma does not show any symptoms as it progresses and cannot be stopped at the later stages, makes it critical to be diagnosed in its early stages. Although various deep learning models have been applied for detecting glaucoma from digital fundus images, due to the scarcity of labeled data, their generalization performance was limited along with high computational complexity and special hardware requirements. In this study, compact Self-Organized Operational Neural Networks (Self-ONNs) are proposed for early detection of glaucoma in fundus images and their performance is compared against the conventional (deep) Convolutional Neural Networks (CNNs) over three benchmark datasets: ACRIMA, RIM-ONE, and ESOGU. The experimental results demonstrate that Self-ONNs not only achieve superior detection performance but can also significantly reduce the computational complexity making it a potentially suitable network model for biomedical datasets especially when the data is scarce.