A Prospective Study on Diabetic Retinopathy Detection Based on Modify Convolutional Neural Network Using Fundus Images at Sindh Institute of Ophthalmology & Visual Sciences
Diabetic Retinopathy (DR) is the most common complication that arises due to diabetes, and it affects the retina. It is the leading cause of blindness globally, and early detection can protect patients from losing sight. However, the early detection of Diabetic Retinopathy is an difficult task that...
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| Materiálatiipa: | Artihkal |
| Giella: | English |
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MDPI AG
2023-01-01
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| Ráidu: | Diagnostics |
| Fáttát: | |
| Liŋkkat: | https://www.mdpi.com/2075-4418/13/3/393 |
| _version_ | 1827760301911572480 |
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| author | Awais Bajwa Neelam Nosheen Khalid Iqbal Talpur Sheeraz Akram |
| author_facet | Awais Bajwa Neelam Nosheen Khalid Iqbal Talpur Sheeraz Akram |
| author_sort | Awais Bajwa |
| collection | DOAJ |
| description | Diabetic Retinopathy (DR) is the most common complication that arises due to diabetes, and it affects the retina. It is the leading cause of blindness globally, and early detection can protect patients from losing sight. However, the early detection of Diabetic Retinopathy is an difficult task that needs clinical experts’ interpretation of fundus images. In this study, a deep learning model was trained and validated on a private dataset and tested in real time at the Sindh Institute of Ophthalmology & Visual Sciences (SIOVS). The intelligent model evaluated the quality of the test images. The implemented model classified the test images into DR-Positive and DR-Negative ones. Furthermore, the results were reviewed by clinical experts to assess the model’s performance. A total number of 398 patients, including 232 male and 166 female patients, were screened for five weeks. The model achieves 93.72% accuracy, 97.30% sensitivity, and 92.90% specificity on the test data as labelled by clinical experts on Diabetic Retinopathy. |
| first_indexed | 2024-03-11T09:49:40Z |
| format | Article |
| id | doaj.art-f90f18f1f8fa412d954353087dffd06d |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T09:49:40Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-f90f18f1f8fa412d954353087dffd06d2023-11-16T16:24:04ZengMDPI AGDiagnostics2075-44182023-01-0113339310.3390/diagnostics13030393A Prospective Study on Diabetic Retinopathy Detection Based on Modify Convolutional Neural Network Using Fundus Images at Sindh Institute of Ophthalmology & Visual SciencesAwais Bajwa0Neelam Nosheen1Khalid Iqbal Talpur2Sheeraz Akram3Ophthalytics, Marietta, GA 30062, USAOphthalytics, Marietta, GA 30062, USASindh Institute of Ophthalmology & Visual Sciences (SIOVS), Hyderabad 71000, PakistanOphthalytics, Marietta, GA 30062, USADiabetic Retinopathy (DR) is the most common complication that arises due to diabetes, and it affects the retina. It is the leading cause of blindness globally, and early detection can protect patients from losing sight. However, the early detection of Diabetic Retinopathy is an difficult task that needs clinical experts’ interpretation of fundus images. In this study, a deep learning model was trained and validated on a private dataset and tested in real time at the Sindh Institute of Ophthalmology & Visual Sciences (SIOVS). The intelligent model evaluated the quality of the test images. The implemented model classified the test images into DR-Positive and DR-Negative ones. Furthermore, the results were reviewed by clinical experts to assess the model’s performance. A total number of 398 patients, including 232 male and 166 female patients, were screened for five weeks. The model achieves 93.72% accuracy, 97.30% sensitivity, and 92.90% specificity on the test data as labelled by clinical experts on Diabetic Retinopathy.https://www.mdpi.com/2075-4418/13/3/393diabetic retinopathy (DR)fundus imagesconvolutional neural networkdeep learningophthalmology |
| spellingShingle | Awais Bajwa Neelam Nosheen Khalid Iqbal Talpur Sheeraz Akram A Prospective Study on Diabetic Retinopathy Detection Based on Modify Convolutional Neural Network Using Fundus Images at Sindh Institute of Ophthalmology & Visual Sciences Diagnostics diabetic retinopathy (DR) fundus images convolutional neural network deep learning ophthalmology |
| title | A Prospective Study on Diabetic Retinopathy Detection Based on Modify Convolutional Neural Network Using Fundus Images at Sindh Institute of Ophthalmology & Visual Sciences |
| title_full | A Prospective Study on Diabetic Retinopathy Detection Based on Modify Convolutional Neural Network Using Fundus Images at Sindh Institute of Ophthalmology & Visual Sciences |
| title_fullStr | A Prospective Study on Diabetic Retinopathy Detection Based on Modify Convolutional Neural Network Using Fundus Images at Sindh Institute of Ophthalmology & Visual Sciences |
| title_full_unstemmed | A Prospective Study on Diabetic Retinopathy Detection Based on Modify Convolutional Neural Network Using Fundus Images at Sindh Institute of Ophthalmology & Visual Sciences |
| title_short | A Prospective Study on Diabetic Retinopathy Detection Based on Modify Convolutional Neural Network Using Fundus Images at Sindh Institute of Ophthalmology & Visual Sciences |
| title_sort | prospective study on diabetic retinopathy detection based on modify convolutional neural network using fundus images at sindh institute of ophthalmology visual sciences |
| topic | diabetic retinopathy (DR) fundus images convolutional neural network deep learning ophthalmology |
| url | https://www.mdpi.com/2075-4418/13/3/393 |
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