Abstract
Diabetic retinopathy is one of the major causes of blindness in the population aged 20–65. In this paper, we address the problem of automatic diabetic retinopathy detection and proposed a novel deep learning hybrid to solve the problem. We use transfer learning on pre-trained Inception-ResNet-v2 and added a custom block of CNN layers on top of Inception-ResNet-v2 for building the hybrid model. We evaluated the performance of the proposed model on Messidor-1 diabetic retinopathy dataset and APTOS 2019 blindness detection (Kaggle dataset). Our model performed better than other published results. We achieved a test accuracy of 72.33% and 82.18% on Messidor-1 and APTOS dataset, respectively.
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Gangwar, A.K., Ravi, V. (2021). Diabetic Retinopathy Detection Using Transfer Learning and Deep Learning. In: Bhateja, V., Peng, SL., Satapathy, S.C., Zhang, YD. (eds) Evolution in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1176. Springer, Singapore. https://doi.org/10.1007/978-981-15-5788-0_64
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DOI: https://doi.org/10.1007/978-981-15-5788-0_64
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