Identifying Retinal Diseases on OCT Image Based on Deep Learning


  • Abdelhafid Errabih
  • Mohyeddine Boussarhane
  • Benayad Nsiri ENSAM-Rabat Mohammed V University
  • Abdelalim Sadiq
  • My Hachem El Yousfi Alaoui
  • Rachid Oulad Haj Thami
  • Brahim Benaji



Artificial intelligence, deep learning, convolutional neural network, transfer learning, optical coherence tomography, DRUSEN, choroidal neovascularization, diabetic macular edema


Computer-aided diagnosis has the potential to replace or at least support medical personnel in their everyday responsibilities such as diagnosis, therapy, and surgery. In the area of ophthalmology, artificial intelligence approaches have been incorporated in the diagnosis of the most frequent ocular disorders, such as choroidal neovascularization (CNV), diabetic macular oedema (DMO), and DRUSEN; these illnesses pose a significant risk of vision loss. Optical coherence tomography (OCT) is an imaging technology used to diagnose the aforementioned eye disorders. It enables ophthalmologists to see the back of the eye and take various slices of the retina. The goal of this research is to automate the diagnosis of retinopathy, which includes CNV, DME, and DRUSEN. The approach employed is a deep learning-based, and transfer learning technique, applying to a public dataset of OCT pictures and two pertained neural network models VGG16 and InceptionV3, which are trained on the big database "ImageNet." That allows them to be able to extract the main features of millions of images. Furthermore, fine-tuning approaches are applied to outperform the feature extraction method, by modifying the hyperparameters. The findings showed that the VGG16 model performed better in classification than the InceptionV3 architecture, with a 0.93 accuracy.




How to Cite

Errabih, A., Boussarhane, M., Nsiri, B., Sadiq, A. ., El Yousfi Alaoui, M. H. ., Oulad Haj Thami, R., & Benaji, B. . (2022). Identifying Retinal Diseases on OCT Image Based on Deep Learning. International Journal of Online and Biomedical Engineering (iJOE), 18(15), pp. 141–159.