Detection of Lung Cancer using SVM Classifier and KNN Algorithm
- Resource Type
- Conference
- Authors
- Sathishkumar, R.; Kalaiarasan, K.; Prabhakaran, A.; Aravind, M.
- Source
- 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN) System, Computation, Automation and Networking (ICSCAN), 2019 IEEE International Conference on. :1-7 Mar, 2019
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Lung
Image segmentation
Feature extraction
Biomedical imaging
Support vector machines
Computed tomography
Diseases
Early detection
XGboost
Segmentation Filteration
classification
- Language
In this computer era we are totally going with the automation of everything, in the same way the medical industry is also automated with the help of image processing and data analytics. The best way to control the death cause by cancer is early detection. The medical image or a CT scan image is pre-processed. The contrast of the image is increased with the CLAHE Equalization technique. Then it is segmented with the help of random walk segmentation method. In segmentation the three process will happen the ROI of image is segmented and then then the border correction is done. As third part the continuous pixel change is segmented. The classification is the major portion where the cancerous and non-cancerous is identified with the pre trained model. All the methods used above deals with the traditional way of image processing and data analytics. In Future this accuracy will be boosted with the modern XGboost algorithm where less data is used to get high accuracy.