Automated Diabetic Retinopathy Detection Based on Binocular Siamese like Convolution Neural Network

Automated Diabetic Retinopathy Detection Based on Binocular Siamese like Convolution Neural Network

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Product Code: Python - Deep Learning
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Product Description

Aim:

                  This mainstay of this paper is to detect the diabetic retinopathy disease identification using deep learning methods.


Synopsis:

                  Diabetic retinopathy (DR) is a common complication of diabetes associated with retinal vascular damage caused by long standing diabetes. Furthermore, the diagnosis of DR mostly depends on the observation and evaluation to fundus photographs of which procedure can be time-consuming even for experienced experts. Therefore computer aided automated diagnosis approaches have great potential in clinical to accurately detect DR in a short time which can further help to improve the screening rate of DR and reduce the number of blindness. For a deep learning model, the most important parts that should be focused on are data set, network architecture and training method. Before being used to train our model, fundus images data set obtained from public resources is preprocessed and augmented. The model accepts two fundus images corresponding to the left eye and right eye as inputs and then transmits them into the Siamese like blocks. The information from two eyes is gathered into the fully connected layer and finally the model will output the diagnosis result of each eye respectively.

Proposed System:

                For a deep learning model, the most important parts that should be focused on are data set, network architecture and training method. Before being used to train our model, fundus images data set obtained from public resources is preprocessed and augmented. The model accepts two fundus images corresponding to the left eye and right eye as inputs and then transmits them into the Siamese-like blocks. The information from two eyes is gathered into the fully-connected layer and finally the model will output the diagnosis result of each eye respectively.


Algorithm Used:

            CNN-Convolution Neural Network

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