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Tariq, Maria, Palade, Vasile and Ma, YingLiang 
ORCID: https://orcid.org/0000-0001-5770-5843
(2023)
Transfer Learning based Classification of Diabetic Retinopathy on the Kaggle EyePACS dataset.
In:
Medical Imaging and Computer-Aided Diagnosis.
Lecture Notes in Electrical Engineering (LNEE)
.
Springer, GBR, 89–99.
ISBN 978-981-16-6774-9
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Abstract
Severe stages of diabetes can eventually lead to an eye condition called diabetic retinopathy. It is one of the leading causes of temporary visual disability and permanent blindness. There is no cure for this disease other than a proper treatment in the early stages. Five stages of diabetic retinopathy are discussed in this paper that need to be detected followed by a proper treatment. Transfer learning is used to detect the grades of diabetic retinopathy in eye fundus images, without training from scratch. The Kaggle EyePACS dataset is one of the largest datasets available publicly for experimentation. In our work, an extensive study on the Kaggle EyePACS dataset is carried out using the pre-trained models ResNet50 and DenseNet121. The Aptos dataset is also used in comparison with this dataset to examine the performance of the pre-trained models. Different experiments are performed to analyze the images from the different classes in the Kaggle EyePACS dataset. This dataset has significant challenges including image noise, imbalanced classes, and incorrect annotations. Our work highlights potential problems within the dataset and the conflicts between the classes. A clustering technique is used to get informative images from the normal class to improve the model’s accuracy to 70%.
| Item Type: | Book Section |
|---|---|
| Uncontrolled Keywords: | deep learning,fine tuning,kaggle eyepacs,pre-trained models,transfer learning,industrial and manufacturing engineering,sdg 3 - good health and well-being ,/dk/atira/pure/subjectarea/asjc/2200/2209 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 16 Jan 2023 11:30 |
| Last Modified: | 20 Dec 2024 01:38 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/90577 |
| DOI: | 10.1007/978-981-16-6775-6_8 |
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