Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics
Diabetic retinopathy (DR) is a fast-spreading disease across the globe, which is caused by diabetes. The DR may lead the diabetic patients to complete vision loss. In this scenario, early identification of DR is more essential to recover the eyesight and provide help for timely treatment. The detect...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9032162/ |
| _version_ | 1818444339601211392 |
|---|---|
| author | Muhammad Mateen Junhao Wen Mehdi Hassan Nasrullah Nasrullah Song Sun Shaukat Hayat |
| author_facet | Muhammad Mateen Junhao Wen Mehdi Hassan Nasrullah Nasrullah Song Sun Shaukat Hayat |
| author_sort | Muhammad Mateen |
| collection | DOAJ |
| description | Diabetic retinopathy (DR) is a fast-spreading disease across the globe, which is caused by diabetes. The DR may lead the diabetic patients to complete vision loss. In this scenario, early identification of DR is more essential to recover the eyesight and provide help for timely treatment. The detection of DR can be manually performed by ophthalmologists and can also be done by an automated system. In the manual system, analysis and explanation of retinal fundus images need ophthalmologists, which is a time-consuming and very expensive task, but in the automated system, artificial intelligence is used to perform an imperative role in the area of ophthalmology and specifically in the early detection of diabetic retinopathy over the traditional detection approaches. Recently, numerous advanced studies related to the identification of DR have been reported. This paper presents a detailed review of the detection of DR with three major aspects; retinal datasets, DR detection methods, and performance evaluation metrics. Furthermore, this study also covers the author's observations and provides future directions in the field of diabetic retinopathy to overcome the research challenges for the research community. |
| first_indexed | 2024-12-14T19:14:22Z |
| format | Article |
| id | doaj.art-0deb6d5d5d4c4aa787d01420d9d601f8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T19:14:22Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0deb6d5d5d4c4aa787d01420d9d601f82022-12-21T22:50:38ZengIEEEIEEE Access2169-35362020-01-018487844881110.1109/ACCESS.2020.29800559032162Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation MetricsMuhammad Mateen0https://orcid.org/0000-0002-2282-5703Junhao Wen1https://orcid.org/0000-0002-6561-560XMehdi Hassan2https://orcid.org/0000-0003-4629-2582Nasrullah Nasrullah3https://orcid.org/0000-0002-4587-4540Song Sun4https://orcid.org/0000-0003-0035-0695Shaukat Hayat5https://orcid.org/0000-0002-4033-5249School of Big Data and Software Engineering, Chongqing University, Chongqing, ChinaSchool of Big Data and Software Engineering, Chongqing University, Chongqing, ChinaDepartment of Computer Science, Air University, Islamabad, PakistanDepartment of Software Engineering, Foundation University Islamabad, Islamabad, PakistanSchool of Big Data and Software Engineering, Chongqing University, Chongqing, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, ChinaDiabetic retinopathy (DR) is a fast-spreading disease across the globe, which is caused by diabetes. The DR may lead the diabetic patients to complete vision loss. In this scenario, early identification of DR is more essential to recover the eyesight and provide help for timely treatment. The detection of DR can be manually performed by ophthalmologists and can also be done by an automated system. In the manual system, analysis and explanation of retinal fundus images need ophthalmologists, which is a time-consuming and very expensive task, but in the automated system, artificial intelligence is used to perform an imperative role in the area of ophthalmology and specifically in the early detection of diabetic retinopathy over the traditional detection approaches. Recently, numerous advanced studies related to the identification of DR have been reported. This paper presents a detailed review of the detection of DR with three major aspects; retinal datasets, DR detection methods, and performance evaluation metrics. Furthermore, this study also covers the author's observations and provides future directions in the field of diabetic retinopathy to overcome the research challenges for the research community.https://ieeexplore.ieee.org/document/9032162/Artificial intelligencedeep learningdiabetic retinopathyfundus imagesmachine learningophthalmology |
| spellingShingle | Muhammad Mateen Junhao Wen Mehdi Hassan Nasrullah Nasrullah Song Sun Shaukat Hayat Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics IEEE Access Artificial intelligence deep learning diabetic retinopathy fundus images machine learning ophthalmology |
| title | Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics |
| title_full | Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics |
| title_fullStr | Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics |
| title_full_unstemmed | Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics |
| title_short | Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics |
| title_sort | automatic detection of diabetic retinopathy a review on datasets methods and evaluation metrics |
| topic | Artificial intelligence deep learning diabetic retinopathy fundus images machine learning ophthalmology |
| url | https://ieeexplore.ieee.org/document/9032162/ |
| work_keys_str_mv | AT muhammadmateen automaticdetectionofdiabeticretinopathyareviewondatasetsmethodsandevaluationmetrics AT junhaowen automaticdetectionofdiabeticretinopathyareviewondatasetsmethodsandevaluationmetrics AT mehdihassan automaticdetectionofdiabeticretinopathyareviewondatasetsmethodsandevaluationmetrics AT nasrullahnasrullah automaticdetectionofdiabeticretinopathyareviewondatasetsmethodsandevaluationmetrics AT songsun automaticdetectionofdiabeticretinopathyareviewondatasetsmethodsandevaluationmetrics AT shaukathayat automaticdetectionofdiabeticretinopathyareviewondatasetsmethodsandevaluationmetrics |