Open AccessClassification of Liver Tumor on Ct Images using Machine Learning
Author(s) -
A. Keerthaan*,
G. Srividhya,
R. Hemalatha
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d5407.118419
Subject(s) - artificial intelligence , support vector machine , liver tumor , computer science , gray level , pattern recognition (psychology) , classifier (uml) , feature (linguistics) , contextual image classification , computer vision , radiology , medicine , image (mathematics) , hepatocellular carcinoma , linguistics , philosophy
Liver tumor is one of the most severe types of cancerous diseases which is responsible for the death of many patients. CT Liver tumor images have more noises which is difficult to diagnose the level of the tumor. It is a challenging task to automatically identify the tumor from CT images because of several anatomical changes in different patients. The tumor is difficult to find because of the presence of objects with same intensity level. In this proposed system, fully automated machine learning is used to detect the liver tumor from CT image. Region growing technique is used to segment the region of interest. The textural feature are extracted from Gray level co-occurrence matrix (GLCM) of the segmented image. Extracted textural features are given as input to the designed SVM classifier system. Performance analysis of SVM classification of CT liver tumor image is studied. This will be useful for physician in better automatic diagnosis of liver tumor from CT images.