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Cardiac-DeepIED: Automatic Pixel-Level Deep Segmentation for Cardiac Bi-Ventricle Using Improved End-to-End Encoder-Decoder Network
Cardiac-DeepIED: Automatic Pixel-Level Deep Segmentation for Cardiac Bi-Ventricle Using Improved End-to-End Encoder-Decoder Network
Cardiac-DeepIED: Automatic Pixel-Level Deep Segmentation for Cardiac Bi-Ventricle Using Improved End-to-End Encoder-Decoder Network

Cardiac-DeepIED: Automatic Pixel-Level Deep Segmentation for Cardiac Bi-Ventricle Using Improved End-to-End Encoder-Decoder Network

₹4,000.00
Product Code: Matlab - Deep Learning
Availability: In Stock
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Product Description

Abstract:

              Accurate segmentation of cardiac bi-ventricle (CBV) from magnetic resonance (MR) images has a great significance to analyze and evaluate the function of the cardiovascular system. However, the majority of cardiac MR images show that the similar intensity distribution in different regions, thus providing a little edge information. The resolution of the feature maps and spatial information after three max-pooling layers ,need to be solved before our model training, so we add two corresponding up sampling layer to restore image sresolution.However the lost spatial information is difficult retrieved. To compensate for the loss of resolution caused by the pooling layer, the Cardiac DeepIED introduces skip connections between its encoder and decoder. In this proposed system the input images are color images means then we are convert to gray scale from the color images.


                The image features like color, weight, and depth and pixel information to apply before the classifier (neural network). Here we used the segmentation algorithm is used in order to segment the portion of defected areas. The neural network concept is used for training the image and testing the image with the help of weight estimating classifier.


Proposed System:

                In this proposed system the input images are color images means then we are convert to gray scale from the color images. The image features like color, weight, and depth and pixel information to apply before the classifier (neural network). Here we used the segmentation algorithm is used in order to segment the portion of defected areas. The neural network concept is used for training the image and testing the image with the help of weight estimating classifier.


 


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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.