Brain Tumor Detection using Deep Learning (Mobilenet)
Objective
The objective of the project is to develop an accurate and reliable deep learning model that can effectively detect brain tumors from MRI images. The project aims to leverage the power of convolutional neural networks and transfer learning using the MobileNet architecture
Abstract
A brain tumor is understood by the scientific community as the growth of abnormal cells in the brain, some of which can lead to cancer. The traditional method to detect brain tumors is nuclear magnetic resonance (MRI). Having the MRI images, information about the uncontrolled growth of tissue in the brain is identified. In several research articles, brain tumor detection is done through the application of Machine Learning and Deep Learning algorithms. When these systems are applied to MRI images, brain tumor prediction is done very quickly and greater accuracy helps to deliver treatment to patients. These predictions also help the radiologist to make quick decisions. In the proposed work, a set of Artificial Neural Networks (ANN) are applied in the detection of the presence of brain tumor, and its performance is analyzed through different metrics.
Keywords: Image processing, brain tumor, Deep learning.
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:
o Acquisition
o Image enhancement
o Image restoration
o Color image processing
o Image compression
o Morphological processing
o Segmentation etc.,
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
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, not projects of or by IEEE.
