Redefining Retinal Lesion Segmentation: A Quantum Leap With DL-UNet Enhanced Auto Encoder-Decoder for Fundus Image Analysis
The first diagnosis of diabetic retinopathy (DR) must include lesion segmentation. As it takes a lot of time and effort to label lesions, automatic segmentation methods have to be created manually. The degree of the retina’s degenerative lesions determines how severe diabetic retinopathy...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10177951/ |
| _version_ | 1797776948159578112 |
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| author | B. Naveen Kumar T. R. Mahesh G. Geetha Suresh Guluwadi |
| author_facet | B. Naveen Kumar T. R. Mahesh G. Geetha Suresh Guluwadi |
| author_sort | B. Naveen Kumar |
| collection | DOAJ |
| description | The first diagnosis of diabetic retinopathy (DR) must include lesion segmentation. As it takes a lot of time and effort to label lesions, automatic segmentation methods have to be created manually. The degree of the retina’s degenerative lesions determines how severe diabetic retinopathy is. A major influence is on the early detection of illness and treatment of DR. To reliably identify the sites of related lesions and identify various abnormalities in retinal fundus pictures, deep learning algorithms are crucial. Additionally, utilizing patch-based analysis, a deep convolutional neural network is constructed. In this study, encoder-decoder neural networks along with channel-wise spatial Attention Mechanisms are proposed. The IDRiD dataset, which includes hard exudate segmentations, is used to train and evaluate the architecture. In this method, image patches are created using the sliding window technique. To determine the effectiveness of the recommended strategy, a thorough experiment was conducted on IDRiD. In order to predict the various sorts of lesions, the trained network analyses the picture patches and creates a probability map. This technique’s efficacy and supremacy are confirmed by the expected accuracy of 99.94 %. The findings of this experiment show significantly enhanced performance in terms of accuracy when compared to prior research on comparable tasks. |
| first_indexed | 2024-03-12T22:57:05Z |
| format | Article |
| id | doaj.art-a789036994cc45ffabfbdabc6d685ecf |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T22:57:05Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a789036994cc45ffabfbdabc6d685ecf2023-07-19T23:00:28ZengIEEEIEEE Access2169-35362023-01-0111708537086410.1109/ACCESS.2023.329444310177951Redefining Retinal Lesion Segmentation: A Quantum Leap With DL-UNet Enhanced Auto Encoder-Decoder for Fundus Image AnalysisB. Naveen Kumar0T. R. Mahesh1https://orcid.org/0000-0002-5589-8992G. Geetha2Suresh Guluwadi3https://orcid.org/0000-0001-7905-3014Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bengaluru, IndiaDepartment of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bengaluru, IndiaDepartment of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bengaluru, IndiaDepartment of Mechanical Engineering, Adama Science and Technology University, Adama, EthiopiaThe first diagnosis of diabetic retinopathy (DR) must include lesion segmentation. As it takes a lot of time and effort to label lesions, automatic segmentation methods have to be created manually. The degree of the retina’s degenerative lesions determines how severe diabetic retinopathy is. A major influence is on the early detection of illness and treatment of DR. To reliably identify the sites of related lesions and identify various abnormalities in retinal fundus pictures, deep learning algorithms are crucial. Additionally, utilizing patch-based analysis, a deep convolutional neural network is constructed. In this study, encoder-decoder neural networks along with channel-wise spatial Attention Mechanisms are proposed. The IDRiD dataset, which includes hard exudate segmentations, is used to train and evaluate the architecture. In this method, image patches are created using the sliding window technique. To determine the effectiveness of the recommended strategy, a thorough experiment was conducted on IDRiD. In order to predict the various sorts of lesions, the trained network analyses the picture patches and creates a probability map. This technique’s efficacy and supremacy are confirmed by the expected accuracy of 99.94 %. The findings of this experiment show significantly enhanced performance in terms of accuracy when compared to prior research on comparable tasks.https://ieeexplore.ieee.org/document/10177951/Diabetic retinopathypatch generationfundus imageslesion segmentationencoder-decoder networkconvolutional neural network |
| spellingShingle | B. Naveen Kumar T. R. Mahesh G. Geetha Suresh Guluwadi Redefining Retinal Lesion Segmentation: A Quantum Leap With DL-UNet Enhanced Auto Encoder-Decoder for Fundus Image Analysis IEEE Access Diabetic retinopathy patch generation fundus images lesion segmentation encoder-decoder network convolutional neural network |
| title | Redefining Retinal Lesion Segmentation: A Quantum Leap With DL-UNet Enhanced Auto Encoder-Decoder for Fundus Image Analysis |
| title_full | Redefining Retinal Lesion Segmentation: A Quantum Leap With DL-UNet Enhanced Auto Encoder-Decoder for Fundus Image Analysis |
| title_fullStr | Redefining Retinal Lesion Segmentation: A Quantum Leap With DL-UNet Enhanced Auto Encoder-Decoder for Fundus Image Analysis |
| title_full_unstemmed | Redefining Retinal Lesion Segmentation: A Quantum Leap With DL-UNet Enhanced Auto Encoder-Decoder for Fundus Image Analysis |
| title_short | Redefining Retinal Lesion Segmentation: A Quantum Leap With DL-UNet Enhanced Auto Encoder-Decoder for Fundus Image Analysis |
| title_sort | redefining retinal lesion segmentation a quantum leap with dl unet enhanced auto encoder decoder for fundus image analysis |
| topic | Diabetic retinopathy patch generation fundus images lesion segmentation encoder-decoder network convolutional neural network |
| url | https://ieeexplore.ieee.org/document/10177951/ |
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