Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction

Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction

₹5,000.00
Product Code: Matlab-Machine Learning
Availability: In Stock
Viewed 3683 times

Product Description

                        Technology: Machine Learning                                    Tool: Matlab  2018a

Objective:


               The main aim of the project is to develop new methods of CT or MRI images of pancreatic ductal adenocarcinoma of a patient



ABSTRACT

             Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancer types worldwide, with the lowest 5-year survival rate among all kinds of cancers. Approximately 60-70% of PDAC arise from the head of the pancreas, whereas 20-25% arise from the body/tail (9). In general, tumors arising from the head of the pancreas come to clinical attention earlier than tumors arising from the body and tail, as the head of the pancreas contains the common bile duct. In this study, state-of-the-art deep learning models were used to develop an automatic framework for PDAC detection, focusing on small lesions. CE-CT  or MRI image scans from a cohort of 119 pathology-proven PDAC patients and a cohort of 123 patients without PDAC were used to train a convnet for automatic lesion detection and segmentation (convent). Finally deep convolution neural networks classifier is applied then result image will compared with the dataset images and it will display whether it is normal or abnormal.


Proposed System


                   We are providing new methods of CT or MRI images of pancreatic ductal adenocarcinoma of a patient. The images are pre processed and further segmented for the required feature. Then Region of interest (ROI) segmentations is applied in order to identify the affected portion of cancer. The binary regions constructed by simple thresholding are deformed by texture and noise. Morphological image processing seeks to achieve the goals of eliminating these defects by accounting for image shape and structure. Then morphological operation algorithm using for segment the cancer cells detected. It is used for pixel values in this image required for segmenting adjusts itself according to the segmented area and position. Finally cnn applied through a deep convolution neural networks then result image will compared with the dataset images and it will display whether it is normal or abnormal.


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.