Lung & Pancreatic Tumor Detection Using DL Techniques

Project Code :TMMAAI157

Objective

The main objective of the project is to detect tumor by using Deep Learning Techniques.

Abstract

In this paper, we proposed a novel technique in order to detect the tumor. Here, we consider the dataset of lungs and pancreas. Spatial transform network is used for the score prediction. Some Deep Learning (DL) methods have been illustrated to reach this goal, including ground truth labeling, semantic segmentation. Use labeled ground truth as training data for machine learning and deep learning models, such as object detectors or semantic segmentation networks. To automate the labeling of ground truth data, you can use a built-in automation algorithm or develop your own algorithm. A semantic segmentation network classifies every pixel in an image, resulting in an image that is segmented by class. The proposed method results gives better classification results when compared with existing approaches. 


Keywords: Tumor, Deep Learning, Semantic Segmentation Networks.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

Software: Matlab 2018a or above

Hardware:

Operating Systems:

  • Windows 10
  • Windows 7 Service Pack 1
  • Windows Server 2019
  • Windows Server 2016

Processors:

Minimum: Any Intel or AMD x86-64 processor

Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support

Disk:

Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation

Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space

RAM:

Minimum: 4 GB

Recommended: 8 GB

Learning Outcomes

  • Introduction to Matlab
  • What is EISPACK & LINPACK
  • How to start with MATLAB
  • About Matlab language
  • Matlab coding skills
  • About tools & libraries
  • Application Program Interface in Matlab
  • About Matlab desktop
  • How to use Matlab editor to create M-Files
  • Features of Matlab
  • Basics on Matlab
  • What is an Image/pixel?
  • About image formats
  • Introduction to Image Processing
  • How digital image is formed
  • Importing the image via image acquisition tools
  • Analyzing and manipulation of image.
  • Phases of image processing:
    • Acquisition
    • Image enhancement
    • Image restoration
    • Color image processing
    • Image compression
    •  Morphological processing
    • Segmentation etc.,
  • How detect & send a mail using Matlab
  • How to extend our work to another real time applications
  • Project development Skills
    • Problem analyzing skills
    • Problem solving skills
    • Creativity and imaginary skills
    • Programming skills
    • Deployment
    • Testing skills
    • Debugging skills
    • Project presentation skills
    • Thesis writing skills

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