Genetic Algorithm based Feature Selection to Enhance Breast Cancer Classification

Project Code :TMMAAI330

Objective

This Objective utilizes genetic algorithms for image clustering, followed by feature extraction and classification using Random Forest to achieve efficient image processing and prediction.

Abstract

This study presents a method for image classification using a hybrid approach combining clustering and machine learning techniques. The process begins by selecting and resizing an image to 32x32 pixels, followed by the extraction of red, green, and blue (RGB) color planes as features. These features are normalized and clustered using a genetic algorithm, which optimizes the selection of cluster centers. The distance between data points and cluster centers is minimized to assign clusters, resulting in a segmented image. The segmented output is then reshaped, and the feature vectors are extracted. These features are used to train a Random Forest classifier, which is validated using pre-loaded feature and label datasets. The model achieves accuracy by leveraging the mean feature values, ultimately yielding a prediction with the trained classifier. This approach demonstrates a practical application of genetic algorithms for clustering and machine learning for image classification, achieving notable accuracy in predicting the class of input images.

Keywords: Machine Learning, Breast Cancer Dataset, Genetic Algorithm

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:

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

Demo Video

Artificial Intelligence

Artificial Neural Network
Deep Learning

Image Processing

Fusion and Saliency
Image Segmentation
Image Compression
Image Detection
Image Retrieval
Security Applications
Image Enhancement
Image Denoising

Communications

Modulation Techniques
5G networks
MIMO Transmissions
Device-to-Device Communications

Wireless Sensor Networks

Routing Concepts
Protocols

Signal Processing

Security Applications
Denoising
Smoothing

Antenna

Reconfigurable Antenna
Patch Antenna