Abstract
Diabetic retinopathy is one of the most common complications of diabetes. If left undetected, it may cause serious damage to the eyes or even vision loss. The major screening techniques for diabetic retinopathy include fundus images, optical coherence tomography and scanning laser ophthalmoscopy. Fundus images are most commonly used for screening purpose. The four main steps of diabetic retinopathy detection using image processing are—applying preprocessing techniques, feeding the image to a neural network, performing feature extraction and description and finally getting the classification results. The preprocessing is done to remove noise and enhance contrast. Then, the image is fed to a neural network where features are extracted and a model is trained. Finally, the model is tested on a set of images, and classification results are obtained. This paper presents the comparative analysis of six recent diabetic retinopathy detection systems using image processing and proposes a diabetic retinopathy detection system based on Inception-V3.
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Tupe, A.D., Joshi, Y.U., Tambe, S.B., Dewan, J.H. (2022). Diabetic Retinopathy Detection Using Image Processing Techniques: A Study. In: Tiwari, S., Trivedi, M.C., Kolhe, M.L., Mishra, K., Singh, B.K. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 318. Springer, Singapore. https://doi.org/10.1007/978-981-16-5689-7_56
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DOI: https://doi.org/10.1007/978-981-16-5689-7_56
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