An automated diabetic retinopathy disorders detection model based on pretrained MobileNetv2 and nested patch division using fundus images

Authors : Hakan YILDIRIM, Ülkü ÇELİKER, Sabiha GÜNGÖR KOBAT, Sengul DOGAN, Mehmet BAYĞIN, Orhan YAMAN, Türker TUNCER, Murat ERDAĞ
Pages : 1741-1746
Doi:10.32322/jhsm.1184981
View : 42 | Download : 11
Publication Date : 2022-10-25
Article Type : Research Paper
Abstract :Aim: Fundus images are very important to diagnose some ophthalmologic disorders. Hence, fundus images have become a very important data source for machine-learning society. Our primary goal is to propose a new automated disorder classification model for diabetic retinopathy insert ignore into journalissuearticles values(DR); using the strength of deep learning. In this model, our proposed model suggests a treatment technique using fundus images. Material and Method: In this research, a new dataset was acquired and this dataset contains 1365 Fundus Fluorescein Angiography images with five classes. To detect these disorders automatically, we proposed a transfer learning-based feature engineering model. This feature engineering model uses pretrained MobileNetv2 and nested patch division to extract deep and exemplar features. The neighborhood component analysis insert ignore into journalissuearticles values(NCA); feature selection function has been applied to choose the top features. k nearest neighbors insert ignore into journalissuearticles values(kNN); classification function has been used to get results and we used 10-fold cross-validation insert ignore into journalissuearticles values(CV); to validate the results. Results: The proposed MobileNetv2 and nested patch-based image classification model attained 87.40% classification accuracy on the collected dataset. Conclusions: The calculated 87.40% classification accuracy for five classes has been demonstrated high classification accuracy of the proposed deep feature engineering model
Keywords : Diabetic retinopathy, fundus image processing, biomedical image classification, artificial intelligence

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