Proceedings of the 2nd International Conference on Industry 4.0 and Artificial Intelligence (ICIAI 2021)

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Detection of Dry and Wet Age-Related Macular Degeneration Using Deep Learning

Authors
Muhammad Muhammad Abdullahi1, mmabdullahi10@gmail.com, Sudeshna Chakraborty2, sudeshna.chakraborty@sharda.ac.in, Preeti Kaushik3, preeti.kaushik@sharda.ac.in, Ben Slama Sami4, benslama.sami@gmail.com
1Department of Computer Science and Engineering, Sharda University, Greater Noida, India.
2Department of Computer Science and Engineering, Lloyd Institute of Engineering and Technology, Greater Noida, India.
3Department of Computer Science and Engineering, Sharda University, Greater Noida, India.
4Department of Computer Information and Technologies, King Abdulaziz University, Jeddah, Saudi Arabia.
Corresponding Author
Muhammad Muhammad Abdullahimmabdullahi10@gmail.com
Available Online 2 February 2022.
DOI
10.2991/aisr.k.220201.037How to use a DOI?
Keywords
Age-Related Macular Degeneration; Deep Learning; Convolutional Neural Network; Optical Coherence Tomography; Residual Neural Network
Abstract

Age-related macular degeneration (AMD) is a retinal disease in elderly people which deteriorate the central part of the retina. It is one of the leading causes of vision loss in the ageing persons. Every day, massive retinal images of patients with AMD are generated using the Optical Coherence Tomography (OCT) and other retinal imaging modalities. It is critical that these images are automatically analysed, so as to reduce the time consumption and over reliance on clinical professionals. The advance stage of AMD which usually causes loss of sight occurs in either dry or wet form. Most of the models developed in previous studies focuses on the classification of AMD infected and normal retinal images. However, in the later stages of AMD it is necessary to determine whether the AMD is dry or wet. Ability to classify between dry and wet Age-Related Macular Degeneration is very crucial to ophthalmologists in therapeutic indication. It determines whether a patient receives Anti-VEGF injection therapy treatment. The objective of this study is to develop a convolutional neural network model that will classify between dry and wet AMD. A pretrained Deep Residual Neural Network with 50-layers (ResNet50) was used to train the model using the KERMANY dataset consisting of 32,931 OCT images of dry and wet AMD. The model was evaluated and it performed with an accuracy of 96.56%, 98.20% Specificity and 89.45% sensitivity respectively.

Copyright
© 2022 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

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Volume Title
Proceedings of the 2nd International Conference on Industry 4.0 and Artificial Intelligence (ICIAI 2021)
Series
Advances in Intelligent Systems Research
Publication Date
2 February 2022
ISBN
10.2991/aisr.k.220201.037
ISSN
1951-6851
DOI
10.2991/aisr.k.220201.037How to use a DOI?
Copyright
© 2022 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

Cite this article

risenwbib
TY  - CONF
AU  - Muhammad Muhammad Abdullahi
AU  - Sudeshna Chakraborty
AU  - Preeti Kaushik
AU  - Ben Slama Sami
PY  - 2022
DA  - 2022/02/02
TI  - Detection of Dry and Wet Age-Related Macular Degeneration Using Deep Learning
BT  - Proceedings of the 2nd International Conference on Industry 4.0 and Artificial Intelligence (ICIAI 2021)
PB  - Atlantis Press
SP  - 211
EP  - 214
SN  - 1951-6851
UR  - https://doi.org/10.2991/aisr.k.220201.037
DO  - 10.2991/aisr.k.220201.037
ID  - Abdullahi2022
ER  -