Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets

Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets

₹5,000.00
Product Code: Matlab-Deep Learning
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Product Description

Technology: Deep Learning                                                   Tool: MATLAB R2018a



Objective:

The main aim of this project is used to identify the COVID or healthy from X-ray images using deep learning techniques.

Abstract:

In the rapidly evolving global pandemic of COVID-19, the use of CXR for COVID-19 diagnosis or triage for patient management has become an important issue to preserve limited medical resources and prevent further spreading of the virus. However, the current diagnostic performance with CXR is not sufficient for routine clinical use, so the need of artificial intelligence to improve diagnostic performance of CXR is increasing. Our analysis found that there are statistically significant differences in the patch-wise intensity distribution, which is well-correlated with the radiological findings of the localized intensity variations in COVID-19 CXR. This findings lead us to propose a novel patch-based neural network architecture with random patch cropping, from which the final classification result are obtained by majority voting from inference results at multiple patch locations.


Proposed System:

The Proposed system focus on tumor segmentation of medical image sequences using back propagation neural network. The proposed work utilizes pattern based classification using neural network function. Active Contour segmentation is designed in the proposed area. Here the threshold required for segmenting adjusts itself according to the segmented area and position.

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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.