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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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Main Authors: Abbood, Saif Hameed, Abdull Hamed, Haza Nuzly, Mohd. Rahim, Mohd. Shafry, Rehman, Amjad, Saba, Tanzila, Ali Bahaj, Saeed
格式: Article
語言:English
出版: Institute of Electrical and Electronics Engineers Inc. 2022
主題:
QA75 Electronic computers. Computer science
在線閱讀:http://eprints.utm.my/104414/1/HazaNuzlyAbdull2022_HybridRetinalImageEnhancementAlgorithm.pdf
http://eprints.utm.my/104414/
http://dx.doi.org/10.1109/ACCESS.2022.3189374
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spelling my.utm.1044142024-02-04T09:55:31Z http://eprints.utm.my/104414/ Hybrid retinal image enhancement algorithm for diabetic retinopathy diagnostic using deep learning model Abbood, Saif Hameed Abdull Hamed, Haza Nuzly Mohd. Rahim, Mohd. Shafry Rehman, Amjad Saba, Tanzila Ali Bahaj, Saeed QA75 Electronic computers. Computer science 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. Institute of Electrical and Electronics Engineers Inc. 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/104414/1/HazaNuzlyAbdull2022_HybridRetinalImageEnhancementAlgorithm.pdf Abbood, Saif Hameed and Abdull Hamed, Haza Nuzly and Mohd. Rahim, Mohd. Shafry and Rehman, Amjad and Saba, Tanzila and Ali Bahaj, Saeed (2022) Hybrid retinal image enhancement algorithm for diabetic retinopathy diagnostic using deep learning model. IEEE Access, 10 (NA). pp. 73079-73086. ISSN 2169-3536 http://dx.doi.org/10.1109/ACCESS.2022.3189374 DOI : 10.1109/ACCESS.2022.3189374
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Abbood, Saif Hameed
Abdull Hamed, Haza Nuzly
Mohd. Rahim, Mohd. Shafry
Rehman, Amjad
Saba, Tanzila
Ali Bahaj, Saeed
Hybrid retinal image enhancement algorithm for diabetic retinopathy diagnostic using deep learning model
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.
format Article
author Abbood, Saif Hameed
Abdull Hamed, Haza Nuzly
Mohd. Rahim, Mohd. Shafry
Rehman, Amjad
Saba, Tanzila
Ali Bahaj, Saeed
author_facet Abbood, Saif Hameed
Abdull Hamed, Haza Nuzly
Mohd. Rahim, Mohd. Shafry
Rehman, Amjad
Saba, Tanzila
Ali Bahaj, Saeed
author_sort Abbood, Saif Hameed
title 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_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_sort hybrid retinal image enhancement algorithm for diabetic retinopathy diagnostic using deep learning model
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2022
url http://eprints.utm.my/104414/1/HazaNuzlyAbdull2022_HybridRetinalImageEnhancementAlgorithm.pdf
http://eprints.utm.my/104414/
http://dx.doi.org/10.1109/ACCESS.2022.3189374
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score 13.250246

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