Diabetic Retinopathy Diagnosis based on Convolutional Neural Network

Abstract

Diabetic Retinopathy DR is a popular disease for many people as a result of age or the diabetic, as a result, it can cause blindness. therefore, diagnosis of this disease especially in the early time can prevent its effect for a lot of patients. To achieve this diagnosis, eye retina must be examined continuously. Therefore, computer-aided tools can be used in the field based on computer vision techniques. Different works have been performed using various machine learning techniques. Convolutional Neural Network is one of the promise methods, so it was for Diabetic Retinopathy detection in this paper. Also, the proposed work contains visual enhancement in the pre-processing phase, then the CNN model is trained to be able for recognition and classification phase, to diagnosis the healthy and unhealthy retina image. Three public dataset DiaretDB0, DiaretDBl and DrimDB were used in practical testing. The implementation of this work based on Matlab-R2019a, deep learning toolbox and deep network designer to design the architecture of the convolutional neural network and train it. The results were evaluated to different metrics; accuracy is one of them. The best accuracy that was achieved: for DiaretDB0 is 100%, DiaretDB1 is 99.495% and DrimDB is 97.55%.

Optimize Weight sharing for Aggregation Model in Federated Learning Environment of Brain Tumor classification

“…The convolutional layer is used for feature extraction of the entire image [12][13]. The second part is the pooling layer which is used to lower the dimensionality of retrieved features, decreasing the complexity and the computing time [14]. The fully connected layer is the ultimate phase in the CNN model, which seeks to achieve linearity in the networks [15].…”