Automatic Detection and Monitoring of DiabeticRetinopathy Using Efficient Convolutional NeuralNetworks and Contrast Limited  Adaptive Histogram Equalization

Automatic Detection and Monitoring of DiabeticRetinopathy Using Efficient Convolutional NeuralNetworks and Contrast Limited Adaptive Histogram Equalization

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Product Description

Aim:

This paper aim to detect the diabetic disease identification using deep learning methods.

Synopsis:

Diabetic Retinopathy is a complication of diabetes that is caused due to the changes in the blood vessels of the retina and is one of the leading causes of blindness in the developed world. Up to the present, Diabetic Retinopathy is still screened manually by ophthalmologist which is a time consuming process and hence this paper aims at automatic diagnosis of the disease into its different stages using deep learning. In our approach, we trained a Deep Convolutional Neural Network model on a large dataset consisting of around 35,000 images to automatically diagnose and thereby classify high resolution fundus images of the retina into five stages based on their severity. Within this paper, an application system is built which takes the input parameters as the patient’s details along with the fundus image of the eye. A trained deep Convolutional neural network model will further extract the features of the fundus images and later with the help of the activation functions like Relu and softmax along with optimizer like Adam an output is obtained. The output obtained from the Convolutional Neural Network (CNN) model and the patient details will collectively make a standardized report.

 

Proposed System:

A model is proposed which uses CNN for the automated detection of DR. DR can be classified into several stages such as normal, mild NPDR- small areas of balloon like swellings in the retinal blood vessels, moderate NPDR swelling & distortion of blood vessels, severe NPDR- blood vessels are blocked & causes abnormal growth factor secretion, PDR- growth factors induce proliferation of new blood vessels in the inner retina. Colored fundus images are the input to the CNN model. CNN removes aberrant noise to recognize features like micro-aneurysms & exudates from the fundus images. The model achieves an accuracy of around 95% for a 2 class classification that is the model detects the presence of DR or not & an accuracy of 85% for a 5 class classification that is if DR is present then it’s severity is also determined. DR stage classification has been regarded as a critical step in the evaluation & management of DR. Lack of effective treatment can lead to vision impairment or even irreversible blindness.

 

 This disease can be diagnosed by examining the fundus images. Deep CNN reduces the complexity of the neural network & so it is widely used in deep learning. Training set of images in the training database is passed to the model for training the CNN model. Here the CNN model used is 2-D CNN sequential model also called keras CNN. The convolution layer of CNN will extract features from the source image. The extracted features are down sampled to reduce the dimensionality of the extracted features so as to get more important features by the pooling layer. These features are flattened by the flatten layer into a vector that forms the input to the fully connected layer. Fully connected layer joins all other layers in the model & activation of features is done. This is very essential for the efficiency of stage wise classification.

 

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  9. Software Links
  10. Reference Papers
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  9. Reference Papers
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  11. Online support

The Delivery time for Hardware Mini projects is 7-8 working days.