Multi Modal Image Fusion Techniques to Detect Brain Tumor

Project Code :TMMAAI168

Objective

The main goal of this work is to design efficient automatic brain tumor classification based on CNN and detection based on YOLO.

Abstract

Various multimodalities in the medical realm, such as computed tomography (CT) and magnetic resonance imaging (MRI), are combined to provide a fused image. Image fusion (IF) is a technique for preserving crucial information by combining all relevant information from numerous images into a single fused image. 

In this work, brain CT and MRI images are fused together and will be classified as normal/abnormal. If the network is detected as abnormal, the part of the tumor region is localized. These works are done by using Convolutional Neural Network, YOLOV2 architecture and image processing techniques. The experimental results are evaluated on the basis of model accuracy.

Keywords: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Convolutional Neural Network, YOLO.

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 2020a 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 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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