@proceedings {62526,
	title = {A deep learning approach for the diabetic retinopathy detection},
	journal = {The Proceedings of the International Conference on Smart City Applications},
	year = {2021},
	month = {2021},
	pages = {459-469},
	publisher = {Springer},
	abstract = {Diabetic retinopathy is a severe retinal disease that can blur or distort the vision of the patient. It is one of the leading causes of blindness. Early detection of diabetic retinopathy can significantly help in the treatment. The recent development in the field of AI and especially Deep learning provides ambitious solutions that can be exploited to predict, forecast and diagnose several diseases in their early phases. This work aims towards finding an automatic way to classify a given set of retina images in order to detect the diabetic retinopathy. Deep learning concepts have been used with a convolutional neural network (CNN) algorithm to build a multi-classification model that can detect and classify disease levels automatically. In this study, a CNN architecture has been applied with several parameters on a dataset of diabetic retinopathy with different structures. At the current stage of this work, obtained results are highly encouraging.},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-94191-8_37},
	author = {Sebti, Riad and Zroug, Siham and KAHLOUL, Laid and BENHARZALLAH, Saber}
}
