Manta Ray Foraging Optimizer with Deep Learning-based Fundus Image Retrieval and Classification for Diabetic Retinopathy Grading
Diabetic Retinopathy (DR) is a major source of sightlessness and permanent visual damage. Manual Analysis of DR is a labor-intensive and costly task that requires skilled ophthalmologists to observe and evaluate DR utilizing digital fundus images. The images can be employed for analysis and disease...
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| Format: | Article |
| Language: | English |
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D. G. Pylarinos
2023-10-01
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| Series: | Engineering, Technology & Applied Science Research |
| Subjects: | |
| Online Access: | http://www.etasr.com/index.php/ETASR/article/view/6226 |
| _version_ | 1797660066098184192 |
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| author | Syed Ibrahim Syed Mahamood Shazuli Arunachalam Saravanan |
| author_facet | Syed Ibrahim Syed Mahamood Shazuli Arunachalam Saravanan |
| author_sort | Syed Ibrahim Syed Mahamood Shazuli |
| collection | DOAJ |
| description |
Diabetic Retinopathy (DR) is a major source of sightlessness and permanent visual damage. Manual Analysis of DR is a labor-intensive and costly task that requires skilled ophthalmologists to observe and evaluate DR utilizing digital fundus images. The images can be employed for analysis and disease screening. This laborious task can gain a great advantage in automated detection by exploiting Artificial Intelligence (AI) techniques. Content-Based Image Retrieval (CBIR) approaches are utilized to retrieve related images in massive databases and are helpful in many application regions and most healthcare systems. With this motivation, this article develops the new Manta Ray Foraging Optimizer with Deep Learning-based Fundus Image Retrieval and Classification (MRFODL-FIRC) approach for the grading of DR. The suggested MRFODL-FIRC model investigates the retinal fundus imaging effectively to retrieve the relevant images and identify class labels. To achieve this, the MRFODL-FIRC technique uses Median Filtering (MF) as a pre-processing step. The Capsule Network (CapsNet) model is used to produce feature vectors with the MRFO algorithm as a hyperparameter optimizer. For the image retrieval process, the Manhattan distance metric is used. Finally, the Variational Autoencoder (VAE) model is used for recognizing and classifying DR. The investigational assessment of the MRFODL-FIRC technique is accomplished on medical DR and the outputs highlighted the improved performance of the MRFODL-FIRC algorithm over the current approaches.
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| first_indexed | 2024-03-11T18:24:23Z |
| format | Article |
| id | doaj.art-c65217d751254c2a97e503c1934b5c20 |
| institution | Directory Open Access Journal |
| issn | 2241-4487 1792-8036 |
| language | English |
| last_indexed | 2024-03-11T18:24:23Z |
| publishDate | 2023-10-01 |
| publisher | D. G. Pylarinos |
| record_format | Article |
| series | Engineering, Technology & Applied Science Research |
| spelling | doaj.art-c65217d751254c2a97e503c1934b5c202023-10-14T05:47:05ZengD. G. PylarinosEngineering, Technology & Applied Science Research2241-44871792-80362023-10-0113510.48084/etasr.6226Manta Ray Foraging Optimizer with Deep Learning-based Fundus Image Retrieval and Classification for Diabetic Retinopathy GradingSyed Ibrahim Syed Mahamood Shazuli0Arunachalam Saravanan1Department of Computer and Information Sciences, Annamalai University, IndiaDepartment of Computer and Information Sciences, Annamalai University, India Diabetic Retinopathy (DR) is a major source of sightlessness and permanent visual damage. Manual Analysis of DR is a labor-intensive and costly task that requires skilled ophthalmologists to observe and evaluate DR utilizing digital fundus images. The images can be employed for analysis and disease screening. This laborious task can gain a great advantage in automated detection by exploiting Artificial Intelligence (AI) techniques. Content-Based Image Retrieval (CBIR) approaches are utilized to retrieve related images in massive databases and are helpful in many application regions and most healthcare systems. With this motivation, this article develops the new Manta Ray Foraging Optimizer with Deep Learning-based Fundus Image Retrieval and Classification (MRFODL-FIRC) approach for the grading of DR. The suggested MRFODL-FIRC model investigates the retinal fundus imaging effectively to retrieve the relevant images and identify class labels. To achieve this, the MRFODL-FIRC technique uses Median Filtering (MF) as a pre-processing step. The Capsule Network (CapsNet) model is used to produce feature vectors with the MRFO algorithm as a hyperparameter optimizer. For the image retrieval process, the Manhattan distance metric is used. Finally, the Variational Autoencoder (VAE) model is used for recognizing and classifying DR. The investigational assessment of the MRFODL-FIRC technique is accomplished on medical DR and the outputs highlighted the improved performance of the MRFODL-FIRC algorithm over the current approaches. http://www.etasr.com/index.php/ETASR/article/view/6226fundus imagesimage classificationdiabetic retinopathydeep learningManta Ray foraging optimization |
| spellingShingle | Syed Ibrahim Syed Mahamood Shazuli Arunachalam Saravanan Manta Ray Foraging Optimizer with Deep Learning-based Fundus Image Retrieval and Classification for Diabetic Retinopathy Grading Engineering, Technology & Applied Science Research fundus images image classification diabetic retinopathy deep learning Manta Ray foraging optimization |
| title | Manta Ray Foraging Optimizer with Deep Learning-based Fundus Image Retrieval and Classification for Diabetic Retinopathy Grading |
| title_full | Manta Ray Foraging Optimizer with Deep Learning-based Fundus Image Retrieval and Classification for Diabetic Retinopathy Grading |
| title_fullStr | Manta Ray Foraging Optimizer with Deep Learning-based Fundus Image Retrieval and Classification for Diabetic Retinopathy Grading |
| title_full_unstemmed | Manta Ray Foraging Optimizer with Deep Learning-based Fundus Image Retrieval and Classification for Diabetic Retinopathy Grading |
| title_short | Manta Ray Foraging Optimizer with Deep Learning-based Fundus Image Retrieval and Classification for Diabetic Retinopathy Grading |
| title_sort | manta ray foraging optimizer with deep learning based fundus image retrieval and classification for diabetic retinopathy grading |
| topic | fundus images image classification diabetic retinopathy deep learning Manta Ray foraging optimization |
| url | http://www.etasr.com/index.php/ETASR/article/view/6226 |
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