Abstract
Diabetic retinopathy (DR) is one among the common disease associated with the human eye that can cause blindness. Detection of DR is very important as the disease will damage the eye with the passage of time. A computer-aided diagnosis-based system is used nowadays to assist the medical practitioner to correctly detect DR during the early stages. In this work, a retinal image’s classification is proposed, which is composed of three major blocks. Initially, the images are preprocessed using CLAHE and DNCNN neural networks, which will reduce the induced noise in the images. Preprocessed images then segmented using morphological and K-mean algorithms. The enhanced images have shown a better peak signal-to-noise ratio. The segmented images are then fed to the proposed EyeNet, which is a transfer learning-based model. The architecture of EyeNet is based on ResNet-18. The network is trained on more than 1500 images from clinical, DRIVE, and STARE databases and has shown an accuracy of 99.76%.
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Acknowledgements
Authors are grateful to Dr.Ramesh’s Super Eye Care & Laser Center, Ludhiana, Punjab, India for providing us the clinical dataset of retinal images of patients. Authors are also thankful IKG PTU, Kapurthala, India for providing us the opportunity to carry out this research work.
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Communicated by Vicente Garcia Diaz.
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Kaur, M., Kamra, A. Detection of retinal abnormalities in fundus image using transfer learning networks. Soft Comput 27, 3411–3425 (2023). https://doi.org/10.1007/s00500-021-06088-3
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DOI: https://doi.org/10.1007/s00500-021-06088-3