Efficient Classification Of Diabetic Retinopathy Using Binary Cnn

Efficient Classification Of Diabetic Retinopathy Using Binary Cnn

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Product Code: Python - Deep Learning
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Product Description

Aim:

To detect the diabetic retinopathy disease in the earlier stage using Deep learning method


Synopsis:

Diabetic Retinopathy is a disease that can lead to partial or complete blindness. Research shows that it contributes around 5 percent of the total cases of blindness. Usually it takes about two weeks for the diagnosis of disease; time and money both are wasted. The proposed system aims to eradicate the above problem. Convolutional Neural Network (CNNs) is widely used in pattern and image recognition problems as they have a number of advantages compared to other techniques. Aim of the project is to provide an automated, suitable and sophisticated approach using Convolutional Neural Network (CNN).


Proposed System:

A model is proposed which uses CNN for the automated detection of Diabetic Retinopathy. The client on their first login has to register themselves on the Web Application. The web Application created by Flask. Once the user logins into the system he gets all the access for predicting the diabetic retinopathy by using the input image. After submitting the inputs, it’s move on to the trained model for comparison. Already trained model were trained by deep learning algorithms. So, we get accuracy results in this project using CNN.

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Package Includes

Software Projects Includes

  1. Demo  Video
  2. Abstract
  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
  12. Online support


The Delivery time for software projects is 2 -3 working days. Some of the software projects will require Hardware interface. Please go through the hardware Requirements in the abstract carefully. The Hardware will take 7-8 Working Days

 

Hardware Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. Datasheets
  6. Circuit Diagrams
  7. Source Code
  8. Screen Shots & Photos
  9. Software Links
  10. Reference Papers
  11. Lit survey
  12. Full Project Documentation
  13. Online support


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

   

Mini Projects: Software Includes

  1. Demo  Video
  2. Abstract
  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
  12. Online support

 

The Delivery time for software Miniprojects is 2 -3 working days.

 

Mini Projects - Hardware includes

  1. Demo  Video
  2. Abstract
  3. PPT
  4. Datasheets
  5. Circuit Diagrams
  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.