Bone Cancer Identification and Separation Using K - Means and KNN Classifiers
Objective
This study integrates K-Means and KNN classifiers to identify bone cancer stages in CT scans. It applies advanced pre-processing, feature extraction via GLCM, and multi-stage classification, ensuring robust and accurate results.
Abstract
This research focuses on the identification and separation of bone cancer through the integration of K-Means and KNN classifiers, leveraging machine learning techniques. The study employs CT scans as input data, emphasizing pre-processing steps such as imnoise, Noise Reduction, Gray Converted Image, Edge Detected Image, and Morphological Operations, along with the imsegkmeans algorithm. Classification into cancerous and normal categories is carried out using KNN and K-Means classifiers. To enhance the discriminatory power, feature extraction is performed using the Gray Level Co-occurrence Matrix (GLCM). Notably, the research extends beyond a binary classification by incorporating a multi-stage classification for cancerous cases, categorizing them into stages 1, 2, and 3. The accuracy of the classification model is a critical metric evaluated to assess the effectiveness of the proposed methodology. The integration of these techniques aims to provide a robust and accurate system for bone cancer identification and staging, contributing to advancements in medical imaging and machine learning applications for oncological diagnoses.
Keywords: KNN, K-Means, CT scan, Machine learning techniques, classification and pre-processing.
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
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.
