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DFTSA-Net: Deep Feature Transfer-Based Stacked Autoencoder Network for DME Diagnosis
Diabetic macular edema (DME) is the most common cause of irreversible vision loss in diabetes patients. Early diagnosis of DME is necessary for effective treatment of the disease. Visual detection of DME in retinal screening images by ophthalmologists is a time-consuming process. Recently, many comp...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8534911/ https://www.ncbi.nlm.nih.gov/pubmed/34681974 http://dx.doi.org/10.3390/e23101251 |
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author | Atteia, Ghada Abdel Samee, Nagwan Zohair Hassan, Hassan |
author_facet | Atteia, Ghada Abdel Samee, Nagwan Zohair Hassan, Hassan |
author_sort | Atteia, Ghada |
collection | PubMed |
description | Diabetic macular edema (DME) is the most common cause of irreversible vision loss in diabetes patients. Early diagnosis of DME is necessary for effective treatment of the disease. Visual detection of DME in retinal screening images by ophthalmologists is a time-consuming process. Recently, many computer-aided diagnosis systems have been developed to assist doctors by detecting DME automatically. In this paper, a new deep feature transfer-based stacked autoencoder neural network system is proposed for the automatic diagnosis of DME in fundus images. The proposed system integrates the power of pretrained convolutional neural networks as automatic feature extractors with the power of stacked autoencoders in feature selection and classification. Moreover, the system enables extracting a large set of features from a small input dataset using four standard pretrained deep networks: ResNet-50, SqueezeNet, Inception-v3, and GoogLeNet. The most informative features are then selected by a stacked autoencoder neural network. The stacked network is trained in a semi-supervised manner and is used for the classification of DME. It is found that the introduced system achieves a maximum classification accuracy of 96.8%, sensitivity of 97.5%, and specificity of 95.5%. The proposed system shows a superior performance over the original pretrained network classifiers and state-of-the-art findings. |
format | Online Article Text |
id | pubmed-8534911 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85349112021-10-23 DFTSA-Net: Deep Feature Transfer-Based Stacked Autoencoder Network for DME Diagnosis Atteia, Ghada Abdel Samee, Nagwan Zohair Hassan, Hassan Entropy (Basel) Article Diabetic macular edema (DME) is the most common cause of irreversible vision loss in diabetes patients. Early diagnosis of DME is necessary for effective treatment of the disease. Visual detection of DME in retinal screening images by ophthalmologists is a time-consuming process. Recently, many computer-aided diagnosis systems have been developed to assist doctors by detecting DME automatically. In this paper, a new deep feature transfer-based stacked autoencoder neural network system is proposed for the automatic diagnosis of DME in fundus images. The proposed system integrates the power of pretrained convolutional neural networks as automatic feature extractors with the power of stacked autoencoders in feature selection and classification. Moreover, the system enables extracting a large set of features from a small input dataset using four standard pretrained deep networks: ResNet-50, SqueezeNet, Inception-v3, and GoogLeNet. The most informative features are then selected by a stacked autoencoder neural network. The stacked network is trained in a semi-supervised manner and is used for the classification of DME. It is found that the introduced system achieves a maximum classification accuracy of 96.8%, sensitivity of 97.5%, and specificity of 95.5%. The proposed system shows a superior performance over the original pretrained network classifiers and state-of-the-art findings. MDPI 2021-09-26 /pmc/articles/PMC8534911/ /pubmed/34681974 http://dx.doi.org/10.3390/e23101251 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Atteia, Ghada Abdel Samee, Nagwan Zohair Hassan, Hassan DFTSA-Net: Deep Feature Transfer-Based Stacked Autoencoder Network for DME Diagnosis |
title | DFTSA-Net: Deep Feature Transfer-Based Stacked Autoencoder Network for DME Diagnosis |
title_full | DFTSA-Net: Deep Feature Transfer-Based Stacked Autoencoder Network for DME Diagnosis |
title_fullStr | DFTSA-Net: Deep Feature Transfer-Based Stacked Autoencoder Network for DME Diagnosis |
title_full_unstemmed | DFTSA-Net: Deep Feature Transfer-Based Stacked Autoencoder Network for DME Diagnosis |
title_short | DFTSA-Net: Deep Feature Transfer-Based Stacked Autoencoder Network for DME Diagnosis |
title_sort | dftsa-net: deep feature transfer-based stacked autoencoder network for dme diagnosis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8534911/ https://www.ncbi.nlm.nih.gov/pubmed/34681974 http://dx.doi.org/10.3390/e23101251 |
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