Breast Cancer Detection Using Extreme Learning machine  Based on Feature Fusion with CNN Deep Features

Breast Cancer Detection Using Extreme Learning machine Based on Feature Fusion with CNN Deep Features

₹4,500.00
Product Code: Python - Deep Learning
Availability: In Stock
Viewed 2658 times

Product Description

Aim:

             To classify benign and malignant breast masses using machine learning algorithm based on Feature Fusion.

 

Synopsis:

              Cancer is a class of diseases, which is driven by changing cell of the body and increase beyond normal grow and control. Breast cancer is the one of the frequent type of cancer. Prognosis of breast cancer recurrence is highly required to raise the survival rate of patient suffering from breast cancer. With the advancement of technologies and machine learning techniques, the cancer diagnosis and detection accuracy has improved. A computer-aided diagnosis system based on mammograms enables early breast Cancer detection, diagnosis, and treatment. However, the accuracy of the existing CAD systems remains unsatisfactory. This project explores a breast CAD method based on feature fusion with Convolution neural network deep features. The results outcome shows that random forest algorithm gives the highest accuracy (97.51%) with lowest error rate then SVM classifier (95.65%).


Proposed system:

             The proposed system has mainly three modules: Pre processing is done by filtering. Segmentation is carried out by active contour  segmentation algorithms. This algorithm comes under medical vision learning. This helps to identify the amount of lesions scattered over the body. Feature extraction is by threshold and finally, Approximate reasoning method to recognize the tumor shape and position in MRI image using classification method.

 

Advantages of proposed system: 

This study enables us to get deep knowledge on machine learning algorithms helpful for predicting the early symptoms.

To reduce user interaction by improving the active contour segmentation performance.

 


When you order from finalyearprojects.in, you will receive a confirmation email. Once your order is shipped, you will be emailed the tracking information for your order's shipment. You can choose your preferred shipping method on the Order Information page during the checkout process.

The total time it takes to receive your order is shown below:

The total delivery time is calculated from the time your order is placed until the time it is delivered to you. Total delivery time is broken down into processing time and shipping time.

Processing time: The time it takes to prepare your item(s) to ship from our warehouse. This includes preparing your items, performing quality checks, and packing for shipment.

Shipping time: The time for your item(s) to tarvel from our warehouse to your destination.

Shipping from your local warehouse is significantly faster. Some charges may apply.

In addition, the transit time depends on where you're located and where your package comes from. If you want to know more information, please contact the customer service. We will settle your problem as soon as possible. Enjoy shopping!

Download Abstract

Click the below button to download the abstract.

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.