Investigation of Machine Learning Methodologies in Microaneurysms Discernment
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Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-030-37218-7_139
Reference8 articles.
1. Chudzik, P., Majumdar, S., Calivá, F., Al-Diri, B., Hunter, A.: Microaneurysm detection using fully convolutional neural networks. Comput. Methods Programs Biomed. 158, 185–192 (2018)
2. Wu, B., Zhu, W., Shi, F., Zhu, S., Chen, X.: Automatic detection of microaneurysms in retinal fundus images. Comput. Med. Imaging Graph. 55, 106–112 (2017)
3. Habib, M.M., Welikala, R.A., Hoppe, A., Owen, C.G., Rudnicka, A.R., Barman, S.A.: Detection of microaneurysms in retinal images using an ensemble classifier. Inform. Med. Unlocked 9, 44–57 (2017)
4. Eftekhari, N., Pourreza, H.R., Masoudi, M., Ghiasi-Shirazi, K., Saeedi, E.: Microaneurysm detection in fundus images using a two-step convolutional neural network. Biomed. Eng. Online 18(1), 67 (2019)
5. Harangi, B., Toth, J., Hajdu, A.: Fusion of deep convolutional neural networks for microaneurysm detection in color fundus images. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, pp. 3705–3708. IEEE, July 2018
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