Automatic Detection and Monitoring of Diabetic Retinopathy Using Efficient Convolutional NeuralNetworks

Automatic Detection and Monitoring of Diabetic Retinopathy Using Efficient Convolutional NeuralNetworks

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Product Code: Python - Deep Learning
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Aim:

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


Abstract:

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.


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. Deep CNN reduces the complexity of the neural network & so it is widely used in deep learning. This is very essential for the efficiency of stage wise classification.

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  2. Abstract
  3. Base paper
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  7. Source Code
  8. Screen Shots & Photos
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  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
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  6. Source Code
  7. Screen Shots & Photos
  8. Software Links
  9. Reference Papers
  10. Full Project Documentation
  11. Online support

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