Simplified Convolutional Neural Network Model for Automatic Classification of Retinal Diseases from Optical Coherence Tomography Images

Authors

  • Noor B. Khalaf Department of Biomedical Eng., College of Engineering, Al-Nahrain University, Baghdad, Iraq
  • Hadeel K. Aljobouri Department of Biomedical Eng., College of Engineering, Al-Nahrain University, Baghdad, Iraq.
  • Mohammed S. Najim  College of Medicine, Al-Mustansiriya University, Baghdad, Iraq.
  • Ilyas Çankaya Electrical and Electronics Engineering Department, Ankara Yıldırım Beyazıt University, Ankara, Turkey.

DOI:

https://doi.org/10.29194/NJES.26040314

Keywords:

Retinal diseases, OCT, CNN, Deep learning, Retinal diseases classification

Abstract

Optical coherence tomography (OCT) allows for direct and immediate imaging of the morphology of retinal tissue. It has become a crucial imaging modality for diagnosing eye problems in ophthalmology. One of the most significant morphological characteristics of the retina is the structure of the retinal layers, which provides important evidence for diagnostic purposes and is related to a variety of retinal diseases.

In this paper, a convolutional neural network (CNN) model is proposed that can identify the difference between a normal retina and three common macular diseases: Diabetic macular edema (DME), Drusen, and Choroidal neovascularization (CNV). This proposed model was trained and tested on an open source dataset of OCT images also with professional disease classifications such as DME, CNV, Drusen, and Normal. The suggested model has achieved 98.3% overall classification accuracy, with only 7 wrong classifications out of 368 test samples. The suggested model significantly outperforms other models that made use of the identical dataset. The final results show that the suggested model is particularly adapted to the detection of retinal disorders in ophthalmology centers.

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Published

01-05-2024

How to Cite

Simplified Convolutional Neural Network Model for Automatic Classification of Retinal Diseases from Optical Coherence Tomography Images. (2024). Al-Nahrain Journal for Engineering Sciences, 26(4), 314-319. https://doi.org/10.29194/NJES.26040314

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