Abstract
In the area of ophthalmology, diabetic retinopathy affects an increasing number of people. Early detection avoids severe diabetic proliferative retinopathy complications. In this paper, we propose a method for binary classification of retinal images using convolutional neural networks architecture. This method is formed to recognize and classify a retinal image as normal or abnormal retina. The paper setup is, first of all a preprocessing step is applied, next by data augmentation, and then a CNN formed, and applied. To train, validate and test the proposed model, we have used a public dataset “Resized version of the Diabetic Retinopathy Kaggle competition dataset” from Kaggle web site. Proposed model has trained using 4000 images of the normal retina and 4000 images of abnormal diabetic retina, and 500 images of the normal retina and 500 images of abnormal diabetic retina for testing. The accuracy Achieves 89% in 100 images of single prediction words.
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El Hossi, A., Skouta, A., Elmoufidi, A., Nachaoui, M. (2021). Applied CNN for Automatic Diabetic Retinopathy Assessment Using Fundus Images. In: Fakir, M., Baslam, M., El Ayachi, R. (eds) Business Intelligence. CBI 2021. Lecture Notes in Business Information Processing, vol 416. Springer, Cham. https://doi.org/10.1007/978-3-030-76508-8_31
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