An Efficient System for Identification of Eye Disease in Fundus Images using a Deep Transfer Learning-based Pre-trained Model

Authors

  • Himanshu Sharma Computer Science & Engineering, Mangalayatan University, Uttar Pradesh, India
  • Javed Wasim Department of Computer Engineering and Applications, Mangalayatan University, Uttar Pradesh, India
  • Pankaj Sharma Computer Science & Engineering Department, Eshan College of Engineering, Mathura, India
Volume: 14 | Issue: 5 | Pages: 17398-17404 | October 2024 | https://doi.org/10.48084/etasr.8408

Abstract

Ophthalmologists rely heavily on retinal fundus imaging to diagnose retinal diseases. Early detection can enhance the likelihood of a cure and also prevent blindness. Retinal fundus images can be used by medical professionals to diagnose retinal conditions such as diabetic retinopathy and retinitis pigmentosa. This study proposes an automated diagnostic approach using a Deep Learning (DL) model to identify fundus images with a high prediction rate. This study aims to use multilabel classification to identify diseases in fundus images. An EfficientNet-B5-based model was trained on a fundus image dataset to classify images as normal, NPDR, and PDR. Image preprocessing was used, including conversion to RGB format, resizing to 224×224, and image filtering using the Gaussian blur algorithm. Additionally, 10-fold cross-validation was used to train and validate the proposed approach. The enhanced EfficientNet-B5 model demonstrated superior validation and training accuracy for eye disease classification compared to existing techniques, achieving 96.04% and 99.54%, respectively. This technology enables early detection and treatment of eye conditions, potentially improving patient outcomes.

Keywords:

vision, eye diseases, fundus images, deep learning, TL, image preprocessing, EfficientNetB5

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How to Cite

[1]
Sharma, H., Wasim, J. and Sharma, P. 2024. An Efficient System for Identification of Eye Disease in Fundus Images using a Deep Transfer Learning-based Pre-trained Model. Engineering, Technology & Applied Science Research. 14, 5 (Oct. 2024), 17398–17404. DOI:https://doi.org/10.48084/etasr.8408.

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