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EDLDR: An Ensemble Deep Learning Technique for Detection and Classification of Diabetic Retinopathy

Diabetic retinopathy (DR) is an ophthalmological disease that causes damage in the blood vessels of the eye. DR causes clotting, lesions or haemorrhage in the light-sensitive region of the retina. Person suffering from DR face loss of vision due to the formation of exudates or lesions in the retina....

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Autores principales: Mondal, Sambit S., Mandal, Nirupama, Singh, Krishna Kant, Singh, Akansha, Izonin, Ivan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9818466/
https://www.ncbi.nlm.nih.gov/pubmed/36611416
http://dx.doi.org/10.3390/diagnostics13010124
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author Mondal, Sambit S.
Mandal, Nirupama
Singh, Krishna Kant
Singh, Akansha
Izonin, Ivan
author_facet Mondal, Sambit S.
Mandal, Nirupama
Singh, Krishna Kant
Singh, Akansha
Izonin, Ivan
author_sort Mondal, Sambit S.
collection PubMed
description Diabetic retinopathy (DR) is an ophthalmological disease that causes damage in the blood vessels of the eye. DR causes clotting, lesions or haemorrhage in the light-sensitive region of the retina. Person suffering from DR face loss of vision due to the formation of exudates or lesions in the retina. The detection of DR is critical to the successful treatment of patients suffering from DR. The retinal fundus images may be used for the detection of abnormalities leading to DR. In this paper, an automated ensemble deep learning model is proposed for the detection and classification of DR. The ensembling of a deep learning model enables better predictions and achieves better performance than any single contributing model. Two deep learning models, namely modified DenseNet101 and ResNeXt, are ensembled for the detection of diabetic retinopathy. The ResNeXt model is an improvement over the existing ResNet models. The model includes a shortcut from the previous block to next block, stacking layers and adapting split–transform–merge strategy. The model has a cardinality parameter that specifies the number of transformations. The DenseNet model gives better feature use efficiency as the dense blocks perform concatenation. The ensembling of these two models is performed using normalization over the classes followed by maximum a posteriori over the class outputs to compute the final class label. The experiments are conducted on two datasets APTOS19 and DIARETDB1. The classifications are carried out for both two classes and five classes. The images are pre-processed using CLAHE method for histogram equalization. The dataset has a high-class imbalance and the images of the non-proliferative type are very low, therefore, GAN-based augmentation technique is used for data augmentation. The results obtained from the proposed method are compared with other existing methods. The comparison shows that the proposed method has higher accuracy, precision and recall for both two classes and five classes. The proposed method has an accuracy of 86.08 for five classes and 96.98% for two classes. The precision and recall for two classes are 0.97. For five classes also, the precision and recall are high, i.e., 0.76 and 0.82, respectively.
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spelling pubmed-98184662023-01-07 EDLDR: An Ensemble Deep Learning Technique for Detection and Classification of Diabetic Retinopathy Mondal, Sambit S. Mandal, Nirupama Singh, Krishna Kant Singh, Akansha Izonin, Ivan Diagnostics (Basel) Article Diabetic retinopathy (DR) is an ophthalmological disease that causes damage in the blood vessels of the eye. DR causes clotting, lesions or haemorrhage in the light-sensitive region of the retina. Person suffering from DR face loss of vision due to the formation of exudates or lesions in the retina. The detection of DR is critical to the successful treatment of patients suffering from DR. The retinal fundus images may be used for the detection of abnormalities leading to DR. In this paper, an automated ensemble deep learning model is proposed for the detection and classification of DR. The ensembling of a deep learning model enables better predictions and achieves better performance than any single contributing model. Two deep learning models, namely modified DenseNet101 and ResNeXt, are ensembled for the detection of diabetic retinopathy. The ResNeXt model is an improvement over the existing ResNet models. The model includes a shortcut from the previous block to next block, stacking layers and adapting split–transform–merge strategy. The model has a cardinality parameter that specifies the number of transformations. The DenseNet model gives better feature use efficiency as the dense blocks perform concatenation. The ensembling of these two models is performed using normalization over the classes followed by maximum a posteriori over the class outputs to compute the final class label. The experiments are conducted on two datasets APTOS19 and DIARETDB1. The classifications are carried out for both two classes and five classes. The images are pre-processed using CLAHE method for histogram equalization. The dataset has a high-class imbalance and the images of the non-proliferative type are very low, therefore, GAN-based augmentation technique is used for data augmentation. The results obtained from the proposed method are compared with other existing methods. The comparison shows that the proposed method has higher accuracy, precision and recall for both two classes and five classes. The proposed method has an accuracy of 86.08 for five classes and 96.98% for two classes. The precision and recall for two classes are 0.97. For five classes also, the precision and recall are high, i.e., 0.76 and 0.82, respectively. MDPI 2022-12-30 /pmc/articles/PMC9818466/ /pubmed/36611416 http://dx.doi.org/10.3390/diagnostics13010124 Text en © 2022 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
Mondal, Sambit S.
Mandal, Nirupama
Singh, Krishna Kant
Singh, Akansha
Izonin, Ivan
EDLDR: An Ensemble Deep Learning Technique for Detection and Classification of Diabetic Retinopathy
title EDLDR: An Ensemble Deep Learning Technique for Detection and Classification of Diabetic Retinopathy
title_full EDLDR: An Ensemble Deep Learning Technique for Detection and Classification of Diabetic Retinopathy
title_fullStr EDLDR: An Ensemble Deep Learning Technique for Detection and Classification of Diabetic Retinopathy
title_full_unstemmed EDLDR: An Ensemble Deep Learning Technique for Detection and Classification of Diabetic Retinopathy
title_short EDLDR: An Ensemble Deep Learning Technique for Detection and Classification of Diabetic Retinopathy
title_sort edldr: an ensemble deep learning technique for detection and classification of diabetic retinopathy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9818466/
https://www.ncbi.nlm.nih.gov/pubmed/36611416
http://dx.doi.org/10.3390/diagnostics13010124
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