학술논문

SVM Based Brain Tumor Detection and Classification System
Document Type
Conference
Source
2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon) Mysore Sub Section International Conference (MysuruCon), 2022 IEEE 2nd. :1-4 Oct, 2022
Subject
Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Support vector machines
Image segmentation
Magnetic resonance imaging
Radiology
Feature extraction
Prediction algorithms
Classification algorithms
Brain Tumor
SVM
Segmentation
Classification
Accuracy
Language
Abstract
The brain is the most significant organ in the body since it controls and regulates all vital bodily activities, and a tumour is a bulk of tissue created by the growth of aberrant cells over time. A brain tumour is a tumour that has either grown or moved into the brain. To yet, no root cause has been found for the development of brain tumours. Malignant brain tumours have a relatively high death rate because they grow in the most important organ of the body, the brain, even though they are not particularly prevalent (1.8% of all recorded malignancies worldwide are brain tumours). Therefore, to reduce mortality, it is crucial to precisely identify brain tumours in their early stages. For the management of brain tumour diagnosis, we have presented a computer-assisted radiology system that will evaluate brain cancers from MRI scans. In this paper, we developed a model that accurately identifies tumours using SVM after segmenting pictures with the Watershed and PSO algorithms, DWT and PCA methods, and feature extraction.