Hybrid Retinal Image Enhancement Algorithm for Diabetic Retinopathy Diagnostic Using Deep Learning Model
Diabetic Retinopathy (DR) is a prevalent acute stage of diabetes mellitus that causes vision-effecting abnormalities on the retina. This will cause blindness if not identified early. Because DR not an irreversible procedure, and only vision is preserved via care. Consequently, Early diagnosis and ca...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9819926/ |
| _version_ | 1811218493872275456 |
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| author | Saif Hameed Abbood Haza Nuzly Abdull Hamed Mohd Shafry Mohd Rahim Amjad Rehman Tanzila Saba Saeed Ali Bahaj |
| author_facet | Saif Hameed Abbood Haza Nuzly Abdull Hamed Mohd Shafry Mohd Rahim Amjad Rehman Tanzila Saba Saeed Ali Bahaj |
| author_sort | Saif Hameed Abbood |
| collection | DOAJ |
| description | Diabetic Retinopathy (DR) is a prevalent acute stage of diabetes mellitus that causes vision-effecting abnormalities on the retina. This will cause blindness if not identified early. Because DR not an irreversible procedure, and only vision is preserved via care. Consequently, Early diagnosis and care with DR will significantly minimize the chance of vision loss. In modern ophthalmology, retinal image analysis has become a popular approach to disease diagnosis. The ophthalmologists and computerized systems extensively employ fundus angiography to detect DR-based clinical signs for early detection of DR. fundus photographs are commonly prone to low contrast, noise, and irregular illumination issues due to the complexity of imaging environments such as imaging variety of angles and light conditions. This research presents an Algorithm for improving the quality of images to strengthen the standard of color fundus images by reducing the noise and improving the contrast. The approach includes two main stages: cropping the images to remove insignificant content, then applying the shape crop and gaussian blurring for noise reduction and contrast improvement. The experimental results are evaluated using two standard datasets EyePACS and MESSIDOR. It’s clearly shown that the outcomes of feature extraction and classification of enhanced images is outperform the results without applying the enhancement approach. The improved algorithm is also tested in smart hospitals as an IoMT application. |
| first_indexed | 2024-04-12T07:10:46Z |
| format | Article |
| id | doaj.art-a154b1074c8d4b74aee84d86468a6a90 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T07:10:46Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a154b1074c8d4b74aee84d86468a6a902022-12-22T03:42:39ZengIEEEIEEE Access2169-35362022-01-0110730797308610.1109/ACCESS.2022.31893749819926Hybrid Retinal Image Enhancement Algorithm for Diabetic Retinopathy Diagnostic Using Deep Learning ModelSaif Hameed Abbood0https://orcid.org/0000-0003-2406-8176Haza Nuzly Abdull Hamed1https://orcid.org/0000-0001-8619-4149Mohd Shafry Mohd Rahim2https://orcid.org/0000-0002-5074-2008Amjad Rehman3https://orcid.org/0000-0002-3817-2655Tanzila Saba4https://orcid.org/0000-0003-3138-3801Saeed Ali Bahaj5https://orcid.org/0000-0003-3406-4320School of Computing, Faculty of Engineering, University Technology Malaysia (UTM), Skudai, Johor, MalaysiaSchool of Computing, Faculty of Engineering, University Technology Malaysia (UTM), Skudai, Johor, MalaysiaSchool of Computing, Faculty of Engineering, University Technology Malaysia (UTM), Skudai, Johor, MalaysiaArtificial Intelligence & Data Analytics Lab (AIDA), CCIS, Prince Sultan University, Riyadh, Saudi ArabiaArtificial Intelligence & Data Analytics Lab (AIDA), CCIS, Prince Sultan University, Riyadh, Saudi ArabiaMIS Department College of Business Administration, Prince Sattam bin Abdulaziz University, Alkharj, Saudi ArabiaDiabetic Retinopathy (DR) is a prevalent acute stage of diabetes mellitus that causes vision-effecting abnormalities on the retina. This will cause blindness if not identified early. Because DR not an irreversible procedure, and only vision is preserved via care. Consequently, Early diagnosis and care with DR will significantly minimize the chance of vision loss. In modern ophthalmology, retinal image analysis has become a popular approach to disease diagnosis. The ophthalmologists and computerized systems extensively employ fundus angiography to detect DR-based clinical signs for early detection of DR. fundus photographs are commonly prone to low contrast, noise, and irregular illumination issues due to the complexity of imaging environments such as imaging variety of angles and light conditions. This research presents an Algorithm for improving the quality of images to strengthen the standard of color fundus images by reducing the noise and improving the contrast. The approach includes two main stages: cropping the images to remove insignificant content, then applying the shape crop and gaussian blurring for noise reduction and contrast improvement. The experimental results are evaluated using two standard datasets EyePACS and MESSIDOR. It’s clearly shown that the outcomes of feature extraction and classification of enhanced images is outperform the results without applying the enhancement approach. The improved algorithm is also tested in smart hospitals as an IoMT application.https://ieeexplore.ieee.org/document/9819926/Image enhancementdeep learningdiabetic retinopathyretinafundus imagehealthcare |
| spellingShingle | Saif Hameed Abbood Haza Nuzly Abdull Hamed Mohd Shafry Mohd Rahim Amjad Rehman Tanzila Saba Saeed Ali Bahaj Hybrid Retinal Image Enhancement Algorithm for Diabetic Retinopathy Diagnostic Using Deep Learning Model IEEE Access Image enhancement deep learning diabetic retinopathy retina fundus image healthcare |
| title | Hybrid Retinal Image Enhancement Algorithm for Diabetic Retinopathy Diagnostic Using Deep Learning Model |
| title_full | Hybrid Retinal Image Enhancement Algorithm for Diabetic Retinopathy Diagnostic Using Deep Learning Model |
| title_fullStr | Hybrid Retinal Image Enhancement Algorithm for Diabetic Retinopathy Diagnostic Using Deep Learning Model |
| title_full_unstemmed | Hybrid Retinal Image Enhancement Algorithm for Diabetic Retinopathy Diagnostic Using Deep Learning Model |
| title_short | Hybrid Retinal Image Enhancement Algorithm for Diabetic Retinopathy Diagnostic Using Deep Learning Model |
| title_sort | hybrid retinal image enhancement algorithm for diabetic retinopathy diagnostic using deep learning model |
| topic | Image enhancement deep learning diabetic retinopathy retina fundus image healthcare |
| url | https://ieeexplore.ieee.org/document/9819926/ |
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