A Mobile Deep Learning Classification Model for Diabetic Retinopathy
The pupil, iris, vitreous, and retina are parts of the eye, where any defect due to physical damage or chronic diseases to these parts of the eye can lead to partial vision loss or complete blindness. Changes in retinal structure due to diabetes or high blood pressure lead to diabetic retinopathy (D...
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| Format: | Article |
| Language: | English |
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Kaunas University of Technology
2024-12-01
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| Series: | Elektronika ir Elektrotechnika |
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| Online Access: | https://eejournal.ktu.lt/index.php/elt/article/view/38674 |
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| author | Daniel Rimaru Antonio Nehme Musaed Alhussein Khaled Mahbub Khusheed Aurangzeb Anas Khan |
| author_facet | Daniel Rimaru Antonio Nehme Musaed Alhussein Khaled Mahbub Khusheed Aurangzeb Anas Khan |
| author_sort | Daniel Rimaru |
| collection | DOAJ |
| description | The pupil, iris, vitreous, and retina are parts of the eye, where any defect due to physical damage or chronic diseases to these parts of the eye can lead to partial vision loss or complete blindness. Changes in retinal structure due to diabetes or high blood pressure lead to diabetic retinopathy (DR). The early diagnosis of DR using computer-aided automated tools is possible due to tremendous advancements in machine and deep learning models in the last decade. Devising and implementing innovative deep learning models for retinal structural analysis is crucial to the early diagnosis of DR and other eye diseases. In this work, we have developed a new approach, which involves the development of a lightweight convolutional neural network (CNN)-based model for segmentation of retinal vessels and a mobile application for DR grading. This paper covers the development process of an Android application that leverages the power of CNN-based deep learning model to detect DR regardless of its stage. To achieve this, two models have been created and compared, the best one having an accuracy of 96.72 %. An Android application has then been developed, that makes calls to this model and then displays the results on screen with a simple-to-understand interface developed using the Kivy framework. |
| format | Article |
| id | doaj-art-0ea55cf4f3bb4aaab7612cf7ce8e02aa |
| institution | Kabale University |
| issn | 1392-1215 2029-5731 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Kaunas University of Technology |
| record_format | Article |
| series | Elektronika ir Elektrotechnika |
| spelling | doaj-art-0ea55cf4f3bb4aaab7612cf7ce8e02aa2025-01-07T13:37:57ZengKaunas University of TechnologyElektronika ir Elektrotechnika1392-12152029-57312024-12-01306455210.5755/j02.eie.3867443928A Mobile Deep Learning Classification Model for Diabetic RetinopathyDaniel Rimaru0Antonio Nehme1Musaed Alhussein2Khaled Mahbub3Khusheed Aurangzeb4Anas Khan5Birmingham City University, College of Computing, Birmingham, UKBirmingham City University, College of Computing, Birmingham, UKDepartment of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaBirmingham City University, College of Computing, Birmingham, UKDepartment of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Electrical Engineering, University of Wah, Punjab, IndiaThe pupil, iris, vitreous, and retina are parts of the eye, where any defect due to physical damage or chronic diseases to these parts of the eye can lead to partial vision loss or complete blindness. Changes in retinal structure due to diabetes or high blood pressure lead to diabetic retinopathy (DR). The early diagnosis of DR using computer-aided automated tools is possible due to tremendous advancements in machine and deep learning models in the last decade. Devising and implementing innovative deep learning models for retinal structural analysis is crucial to the early diagnosis of DR and other eye diseases. In this work, we have developed a new approach, which involves the development of a lightweight convolutional neural network (CNN)-based model for segmentation of retinal vessels and a mobile application for DR grading. This paper covers the development process of an Android application that leverages the power of CNN-based deep learning model to detect DR regardless of its stage. To achieve this, two models have been created and compared, the best one having an accuracy of 96.72 %. An Android application has then been developed, that makes calls to this model and then displays the results on screen with a simple-to-understand interface developed using the Kivy framework.https://eejournal.ktu.lt/index.php/elt/article/view/38674diabetic retinopathydeep neural networksmachine learningretinal vessel segmentation |
| spellingShingle | Daniel Rimaru Antonio Nehme Musaed Alhussein Khaled Mahbub Khusheed Aurangzeb Anas Khan A Mobile Deep Learning Classification Model for Diabetic Retinopathy Elektronika ir Elektrotechnika diabetic retinopathy deep neural networks machine learning retinal vessel segmentation |
| title | A Mobile Deep Learning Classification Model for Diabetic Retinopathy |
| title_full | A Mobile Deep Learning Classification Model for Diabetic Retinopathy |
| title_fullStr | A Mobile Deep Learning Classification Model for Diabetic Retinopathy |
| title_full_unstemmed | A Mobile Deep Learning Classification Model for Diabetic Retinopathy |
| title_short | A Mobile Deep Learning Classification Model for Diabetic Retinopathy |
| title_sort | mobile deep learning classification model for diabetic retinopathy |
| topic | diabetic retinopathy deep neural networks machine learning retinal vessel segmentation |
| url | https://eejournal.ktu.lt/index.php/elt/article/view/38674 |
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