Improving diabetic retinopathy classification using transfer learning and optimized deep learning models
Diabetic retinopathy (DR) is an eye disease closely related to diabetes that may lead to severe vision loss or even blindness if not diagnosed and treated in time. With the increasing number of diabetic patients, DR has gradually become a global public health problem. Against this background, it is...
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| Format: | Final Year Project / Dissertation / Thesis |
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2024
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| Online Access: | http://eprints.utar.edu.my/6952/ http://eprints.utar.edu.my/6952/1/fyp_CS_2024_DC.pdf |
| _version_ | 1848886810754678784 |
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| author | Dai, Cheng Xiao |
| author_facet | Dai, Cheng Xiao |
| author_sort | Dai, Cheng Xiao |
| building | UTAR Institutional Repository |
| collection | Online Access |
| description | Diabetic retinopathy (DR) is an eye disease closely related to diabetes that may lead to severe vision loss or even blindness if not diagnosed and treated in time. With the increasing number of diabetic patients, DR has gradually become a global public health problem. Against this background, it is essential to develop a method that can accurately and efficiently diagnose DR. With the continuous advancement of Artificial Intelligence (AI) technology, deep learning and transfer learning have become powerful tools for modern medical research, especially in medical image analysis. This research project aims to combine transfer learning and deep learning techniques to solve this challenge. The main focus is to address the problem of insufficient labelled image datasets in the medical field through transfer learning, exploring how to use limited labelled data to train high-performance models effectively. It will also delve into the use of optimized deep learning models to improve the classification accuracy of DR. It is expected to provide a more accurate and efficient tool for the early diagnosis and treatment of DR, thus helping to decrease vision loss and related complications caused by DR. |
| first_indexed | 2025-11-15T19:44:25Z |
| format | Final Year Project / Dissertation / Thesis |
| id | utar-6952 |
| institution | Universiti Tunku Abdul Rahman |
| institution_category | Local University |
| last_indexed | 2025-11-15T19:44:25Z |
| publishDate | 2024 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utar-69522025-02-27T07:02:44Z Improving diabetic retinopathy classification using transfer learning and optimized deep learning models Dai, Cheng Xiao L Education (General) LB Theory and practice of education T Technology (General) Diabetic retinopathy (DR) is an eye disease closely related to diabetes that may lead to severe vision loss or even blindness if not diagnosed and treated in time. With the increasing number of diabetic patients, DR has gradually become a global public health problem. Against this background, it is essential to develop a method that can accurately and efficiently diagnose DR. With the continuous advancement of Artificial Intelligence (AI) technology, deep learning and transfer learning have become powerful tools for modern medical research, especially in medical image analysis. This research project aims to combine transfer learning and deep learning techniques to solve this challenge. The main focus is to address the problem of insufficient labelled image datasets in the medical field through transfer learning, exploring how to use limited labelled data to train high-performance models effectively. It will also delve into the use of optimized deep learning models to improve the classification accuracy of DR. It is expected to provide a more accurate and efficient tool for the early diagnosis and treatment of DR, thus helping to decrease vision loss and related complications caused by DR. 2024-06 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6952/1/fyp_CS_2024_DC.pdf Dai, Cheng Xiao (2024) Improving diabetic retinopathy classification using transfer learning and optimized deep learning models. Final Year Project, UTAR. http://eprints.utar.edu.my/6952/ |
| spellingShingle | L Education (General) LB Theory and practice of education T Technology (General) Dai, Cheng Xiao Improving diabetic retinopathy classification using transfer learning and optimized deep learning models |
| title | Improving diabetic retinopathy classification using transfer learning and optimized deep learning models |
| title_full | Improving diabetic retinopathy classification using transfer learning and optimized deep learning models |
| title_fullStr | Improving diabetic retinopathy classification using transfer learning and optimized deep learning models |
| title_full_unstemmed | Improving diabetic retinopathy classification using transfer learning and optimized deep learning models |
| title_short | Improving diabetic retinopathy classification using transfer learning and optimized deep learning models |
| title_sort | improving diabetic retinopathy classification using transfer learning and optimized deep learning models |
| topic | L Education (General) LB Theory and practice of education T Technology (General) |
| url | http://eprints.utar.edu.my/6952/ http://eprints.utar.edu.my/6952/1/fyp_CS_2024_DC.pdf |