Transfer Learning based Classification of Diabetic Retinopathy on the Kaggle EyePACS dataset

Tariq, Maria, Palade, Vasile and Ma, YingLiang ORCID: (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

[thumbnail of Maria_MICAD2022] PDF (Maria_MICAD2022) - Accepted Version
Restricted to Repository staff only until 20 December 2024.
Available under License Other licence.

Request a copy


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
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 16 Jan 2023 11:30
Last Modified: 13 Jan 2024 01:19
DOI: 10.1007/978-981-16-6775-6_8

Actions (login required)

View Item View Item