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AOCT-NET: a convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images

  • Ali Mohammad AlqudahEmail author
Original Article

Abstract

Since introducing optical coherence tomography (OCT) technology for 2D eye imaging, it has become one of the most important and widely used imaging modalities for the noninvasive assessment of retinal eye diseases. Age-related macular degeneration (AMD) and diabetic macular edema eye disease are the leading causes of blindness being diagnosed using OCT. Recently, by developing machine learning and deep learning techniques, the classification of eye retina diseases using OCT images has become quite a challenge. In this paper, a novel automated convolutional neural network (CNN) architecture for a multiclass classification system based on spectral-domain optical coherence tomography (SD-OCT) has been proposed. The system used to classify five types of retinal diseases (age-related macular degeneration (AMD), choroidal neovascularization (CNV), diabetic macular edema (DME), and drusen) in addition to normal cases. The proposed CNN architecture with a softmax classifier overall correctly identified 100% of cases with AMD, 98.86% of cases with CNV, 99.17% cases with DME, 98.97% cases with drusen, and 99.15% cases of normal with an overall accuracy of 95.30%. This architecture is a potentially impactful tool for the diagnosis of retinal diseases using SD-OCT images.

Keywords

Retina Optical coherence tomography Spectral domain Classification Deep learning 

Notes

Acknowledgments

The author would like to thank the Nvidia Learning Center at Yarmouk University (YU), represented by Dr. Ahmad Alomari, for providing access to the GPU Unit for training and testing the CNN architecture on the whole image dataset and getting the results. Also, the author would like to thank Zain Innovation Campus (ZINC) at Yarmouk University (YU) represented by Eng. Nour Al-Ajlouni, for providing access to the GPU Unit for initial results and testing of the CNN architecture on part of the dataset.

Availability of data and materials

The image dataset that used to test and support the findings of this research is available from different hospitals in Jordan, but restrictions apply to the availability of these data, which were used under license for the current research only, and so are not publicly available. Data are however may be available from the author upon reasonable request and with permission of all hospitals.

Compliance with ethical standards

Conflict of interest

The author declares that he has no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by the author.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© International Federation for Medical and Biological Engineering 2019

Authors and Affiliations

  1. 1.Department of Biomedical Systems and Informatics EngineeringYarmouk UniversityIrbidJordan

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