3D U-Net for Brain Tumor Segmentation
Objective
The main objective of this project is to segment the tumor in Brain using U-Net architecture which is a deep learning techniques.
Abstract
We describe a fully automated brain tumor segmentation approach based on Convolutional Neural Network in this paper. The suggested network takes the 3D Flair Magnetic Resonance Image (MRI) of glioblastomas as input. These tumors can appear anywhere in the brain and have practically any shape or size by their very nature.
These factors compel us to
investigate an artificial intelligence system that takes advantage of a
flexible, high-capacity neural network while remaining incredibly
efficient. We describe the U-Net model that we've found to be important
for achieving effective performance in segmenting the tumor in brain and
the stage of the patient.
Keywords: Convolutional Neural Network, U-Net architecture, 3D volumes, Brain Tumor (Gliomas), Segmentation
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 & Hardware Requirements:
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 Math Works products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
Learning outcomes:
- Introduction to Matlab
- 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
Related Projects
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects, are not associated or affiliated with IEEE, in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, and not projects of or by IEEE.
