Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey
Diabetes Mellitus, or Diabetes, is a disease in which a person's body fails to respond to insulin released by their pancreas, or it does not produce sufficient insulin. People suffering from diabetes are at high risk of developing various eye diseases over time. As a result of advances in machi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9163123/ |
| _version_ | 1818734577708957696 |
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| author | Rubina Sarki Khandakar Ahmed Hua Wang Yanchun Zhang |
| author_facet | Rubina Sarki Khandakar Ahmed Hua Wang Yanchun Zhang |
| author_sort | Rubina Sarki |
| collection | DOAJ |
| description | Diabetes Mellitus, or Diabetes, is a disease in which a person's body fails to respond to insulin released by their pancreas, or it does not produce sufficient insulin. People suffering from diabetes are at high risk of developing various eye diseases over time. As a result of advances in machine learning techniques, early detection of diabetic eye disease using an automated system brings substantial benefits over manual detection. A variety of advanced studies relating to the detection of diabetic eye disease have recently been published. This article presents a systematic survey of automated approaches to diabetic eye disease detection from several aspects, namely: i) available datasets, ii) image preprocessing techniques, iii) deep learning models and iv) performance evaluation metrics. The survey provides a comprehensive synopsis of diabetic eye disease detection approaches, including state of the art field approaches, which aim to provide valuable insight into research communities, healthcare professionals and patients with diabetes. |
| first_indexed | 2024-12-18T00:07:35Z |
| format | Article |
| id | doaj.art-8721f5ba05e44e21841d16df93746d06 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T00:07:35Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8721f5ba05e44e21841d16df93746d062022-12-21T21:27:46ZengIEEEIEEE Access2169-35362020-01-01815113315114910.1109/ACCESS.2020.30152589163123Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A SurveyRubina Sarki0https://orcid.org/0000-0001-5018-9567Khandakar Ahmed1https://orcid.org/0000-0003-1043-2029Hua Wang2https://orcid.org/0000-0002-8465-0996Yanchun Zhang3https://orcid.org/0000-0002-5094-5980Institute for Sustainable Industries and Liveable Cities, Victoria University, Footscray, VIC, AustraliaInstitute for Sustainable Industries and Liveable Cities, Victoria University, Footscray, VIC, AustraliaInstitute for Sustainable Industries and Liveable Cities, Victoria University, Footscray, VIC, AustraliaInstitute for Sustainable Industries and Liveable Cities, Victoria University, Footscray, VIC, AustraliaDiabetes Mellitus, or Diabetes, is a disease in which a person's body fails to respond to insulin released by their pancreas, or it does not produce sufficient insulin. People suffering from diabetes are at high risk of developing various eye diseases over time. As a result of advances in machine learning techniques, early detection of diabetic eye disease using an automated system brings substantial benefits over manual detection. A variety of advanced studies relating to the detection of diabetic eye disease have recently been published. This article presents a systematic survey of automated approaches to diabetic eye disease detection from several aspects, namely: i) available datasets, ii) image preprocessing techniques, iii) deep learning models and iv) performance evaluation metrics. The survey provides a comprehensive synopsis of diabetic eye disease detection approaches, including state of the art field approaches, which aim to provide valuable insight into research communities, healthcare professionals and patients with diabetes.https://ieeexplore.ieee.org/document/9163123/Diabetic eye diseasediabetic retinopathydeep leaningglaucomaimage processingmacular edema |
| spellingShingle | Rubina Sarki Khandakar Ahmed Hua Wang Yanchun Zhang Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey IEEE Access Diabetic eye disease diabetic retinopathy deep leaning glaucoma image processing macular edema |
| title | Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey |
| title_full | Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey |
| title_fullStr | Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey |
| title_full_unstemmed | Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey |
| title_short | Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey |
| title_sort | automatic detection of diabetic eye disease through deep learning using fundus images a survey |
| topic | Diabetic eye disease diabetic retinopathy deep leaning glaucoma image processing macular edema |
| url | https://ieeexplore.ieee.org/document/9163123/ |
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