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Data Diversity in Convolutional Neural Network Based Ensemble Model for Diabetic Retinopathy

The medical and healthcare domains require automatic diagnosis systems (ADS) for the identification of health problems with technological advancements. Biomedical imaging is one of the techniques used in computer-aided diagnosis systems. Ophthalmologists examine fundus images (FI) to detect and clas...

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Autores principales: Inamullah, Hassan, Saima, Alrajeh, Nabil A., Mohammed, Emad A., Khan, Shafiullah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10204421/
https://www.ncbi.nlm.nih.gov/pubmed/37218773
http://dx.doi.org/10.3390/biomimetics8020187
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author Inamullah,
Hassan, Saima
Alrajeh, Nabil A.
Mohammed, Emad A.
Khan, Shafiullah
author_facet Inamullah,
Hassan, Saima
Alrajeh, Nabil A.
Mohammed, Emad A.
Khan, Shafiullah
author_sort Inamullah,
collection PubMed
description The medical and healthcare domains require automatic diagnosis systems (ADS) for the identification of health problems with technological advancements. Biomedical imaging is one of the techniques used in computer-aided diagnosis systems. Ophthalmologists examine fundus images (FI) to detect and classify stages of diabetic retinopathy (DR). DR is a chronic disease that appears in patients with long-term diabetes. Unattained patients can lead to severe conditions of DR, such as retinal eye detachments. Therefore, early detection and classification of DR are crucial to ward off advanced stages of DR and preserve the vision. Data diversity in an ensemble model refers to the use of multiple models trained on different subsets of data to improve the ensemble’s overall performance. In the context of an ensemble model based on a convolutional neural network (CNN) for diabetic retinopathy, this could involve training multiple CNNs on various subsets of retinal images, including images from different patients or those captured using distinct imaging techniques. By combining the predictions of these multiple models, the ensemble model can potentially make more accurate predictions than a single prediction. In this paper, an ensemble model (EM) of three CNN models is proposed for limited and imbalanced DR data using data diversity. Detecting the Class 1 stage of DR is important to control this fatal disease in time. CNN-based EM is incorporated to classify the five classes of DR while giving attention to the early stage, i.e., Class 1. Furthermore, data diversity is created by applying various augmentation and generation techniques with affine transformation. Compared to the single model and other existing work, the proposed EM has achieved better multi-class classification accuracy, precision, sensitivity, and specificity of 91.06%, 91.00%, 95.01%, and 98.38%, respectively.
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spelling pubmed-102044212023-05-24 Data Diversity in Convolutional Neural Network Based Ensemble Model for Diabetic Retinopathy Inamullah, Hassan, Saima Alrajeh, Nabil A. Mohammed, Emad A. Khan, Shafiullah Biomimetics (Basel) Article The medical and healthcare domains require automatic diagnosis systems (ADS) for the identification of health problems with technological advancements. Biomedical imaging is one of the techniques used in computer-aided diagnosis systems. Ophthalmologists examine fundus images (FI) to detect and classify stages of diabetic retinopathy (DR). DR is a chronic disease that appears in patients with long-term diabetes. Unattained patients can lead to severe conditions of DR, such as retinal eye detachments. Therefore, early detection and classification of DR are crucial to ward off advanced stages of DR and preserve the vision. Data diversity in an ensemble model refers to the use of multiple models trained on different subsets of data to improve the ensemble’s overall performance. In the context of an ensemble model based on a convolutional neural network (CNN) for diabetic retinopathy, this could involve training multiple CNNs on various subsets of retinal images, including images from different patients or those captured using distinct imaging techniques. By combining the predictions of these multiple models, the ensemble model can potentially make more accurate predictions than a single prediction. In this paper, an ensemble model (EM) of three CNN models is proposed for limited and imbalanced DR data using data diversity. Detecting the Class 1 stage of DR is important to control this fatal disease in time. CNN-based EM is incorporated to classify the five classes of DR while giving attention to the early stage, i.e., Class 1. Furthermore, data diversity is created by applying various augmentation and generation techniques with affine transformation. Compared to the single model and other existing work, the proposed EM has achieved better multi-class classification accuracy, precision, sensitivity, and specificity of 91.06%, 91.00%, 95.01%, and 98.38%, respectively. MDPI 2023-04-30 /pmc/articles/PMC10204421/ /pubmed/37218773 http://dx.doi.org/10.3390/biomimetics8020187 Text en © 2023 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
Inamullah,
Hassan, Saima
Alrajeh, Nabil A.
Mohammed, Emad A.
Khan, Shafiullah
Data Diversity in Convolutional Neural Network Based Ensemble Model for Diabetic Retinopathy
title Data Diversity in Convolutional Neural Network Based Ensemble Model for Diabetic Retinopathy
title_full Data Diversity in Convolutional Neural Network Based Ensemble Model for Diabetic Retinopathy
title_fullStr Data Diversity in Convolutional Neural Network Based Ensemble Model for Diabetic Retinopathy
title_full_unstemmed Data Diversity in Convolutional Neural Network Based Ensemble Model for Diabetic Retinopathy
title_short Data Diversity in Convolutional Neural Network Based Ensemble Model for Diabetic Retinopathy
title_sort data diversity in convolutional neural network based ensemble model for diabetic retinopathy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10204421/
https://www.ncbi.nlm.nih.gov/pubmed/37218773
http://dx.doi.org/10.3390/biomimetics8020187
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