Exudate Detection for Diabetic Retinopathy Using Pretrained Convolutional Neural Networks
In the field of ophthalmology, diabetic retinopathy (DR) is a major cause of blindness. DR is based on retinal lesions including exudate. Exudates have been found to be one of the signs and serious DR anomalies, so the proper detection of these lesions and the treatment should be done immediately to...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/5801870 |
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| author | Muhammad Mateen Junhao Wen Nasrullah Nasrullah Song Sun Shaukat Hayat |
| author_facet | Muhammad Mateen Junhao Wen Nasrullah Nasrullah Song Sun Shaukat Hayat |
| author_sort | Muhammad Mateen |
| collection | DOAJ |
| description | In the field of ophthalmology, diabetic retinopathy (DR) is a major cause of blindness. DR is based on retinal lesions including exudate. Exudates have been found to be one of the signs and serious DR anomalies, so the proper detection of these lesions and the treatment should be done immediately to prevent loss of vision. In this paper, pretrained convolutional neural network- (CNN-) based framework has been proposed for the detection of exudate. Recently, deep CNNs were individually applied to solve the specific problems. But, pretrained CNN models with transfer learning can utilize the previous knowledge to solve the other related problems. In the proposed approach, initially data preprocessing is performed for standardization of exudate patches. Furthermore, region of interest (ROI) localization is used to localize the features of exudates, and then transfer learning is performed for feature extraction using pretrained CNN models (Inception-v3, Residual Network-50, and Visual Geometry Group Network-19). Moreover, the fused features from fully connected (FC) layers are fed into the softmax classifier for exudate classification. The performance of proposed framework has been analyzed using two well-known publicly available databases such as e-Ophtha and DIARETDB1. The experimental results demonstrate that the proposed pretrained CNN-based framework outperforms the existing techniques for the detection of exudates. |
| format | Article |
| id | doaj-art-e4be85e4e36749f4ae9ae6be6b09d408 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-e4be85e4e36749f4ae9ae6be6b09d4082025-08-20T02:21:18ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/58018705801870Exudate Detection for Diabetic Retinopathy Using Pretrained Convolutional Neural NetworksMuhammad Mateen0Junhao Wen1Nasrullah Nasrullah2Song Sun3Shaukat Hayat4School of Big Data & Software Engineering, Chongqing University, Chongqing 401331, ChinaSchool of Big Data & Software Engineering, Chongqing University, Chongqing 401331, ChinaDepartment of Software Engineering, Foundation University, Islamabad, 44000, PakistanSchool of Big Data & Software Engineering, Chongqing University, Chongqing 401331, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, ChinaIn the field of ophthalmology, diabetic retinopathy (DR) is a major cause of blindness. DR is based on retinal lesions including exudate. Exudates have been found to be one of the signs and serious DR anomalies, so the proper detection of these lesions and the treatment should be done immediately to prevent loss of vision. In this paper, pretrained convolutional neural network- (CNN-) based framework has been proposed for the detection of exudate. Recently, deep CNNs were individually applied to solve the specific problems. But, pretrained CNN models with transfer learning can utilize the previous knowledge to solve the other related problems. In the proposed approach, initially data preprocessing is performed for standardization of exudate patches. Furthermore, region of interest (ROI) localization is used to localize the features of exudates, and then transfer learning is performed for feature extraction using pretrained CNN models (Inception-v3, Residual Network-50, and Visual Geometry Group Network-19). Moreover, the fused features from fully connected (FC) layers are fed into the softmax classifier for exudate classification. The performance of proposed framework has been analyzed using two well-known publicly available databases such as e-Ophtha and DIARETDB1. The experimental results demonstrate that the proposed pretrained CNN-based framework outperforms the existing techniques for the detection of exudates.http://dx.doi.org/10.1155/2020/5801870 |
| spellingShingle | Muhammad Mateen Junhao Wen Nasrullah Nasrullah Song Sun Shaukat Hayat Exudate Detection for Diabetic Retinopathy Using Pretrained Convolutional Neural Networks Complexity |
| title | Exudate Detection for Diabetic Retinopathy Using Pretrained Convolutional Neural Networks |
| title_full | Exudate Detection for Diabetic Retinopathy Using Pretrained Convolutional Neural Networks |
| title_fullStr | Exudate Detection for Diabetic Retinopathy Using Pretrained Convolutional Neural Networks |
| title_full_unstemmed | Exudate Detection for Diabetic Retinopathy Using Pretrained Convolutional Neural Networks |
| title_short | Exudate Detection for Diabetic Retinopathy Using Pretrained Convolutional Neural Networks |
| title_sort | exudate detection for diabetic retinopathy using pretrained convolutional neural networks |
| url | http://dx.doi.org/10.1155/2020/5801870 |
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