Classification of Liver Tumor on Ct Images using Machine Learning
A.Keerthana1, G.Srividhya2, R.J.Hemalatha3, Jaya Rubi4
1A.Keerthaan, Biomedical Engineering department, Vels Institute of Science,technology and Advanced Studies, Pallavaram ,Chennai, India.
2G.Srividhya, Biomedical Engineering department, Vels Institute of Science, technology and Advanced Studies, Pallavaram, Chennai, India.
3R.J.Hemalatha, Biomedical Engineering department, Vels Institute of Science, technology and Advanced Studies, Pallavaram ,Chennai, India.
4Jaya Rubi, Biomedical Engineering department, Vels Institute of Science,technology and Advanced Studies, Pallavaram ,Chennai, India.
Manuscript received on November 11, 2019. | Revised Manuscript received on November 20 2019. | Manuscript published on 30 November, 2019. | PP: 11336-11338 | Volume-8 Issue-4, November 2019. | Retrieval Number: D5407118419/2019©BEIESP | DOI: 10.35940/ijrte.D5407.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: CT Liver Image, Region Growing, Gray Level Co-occurrence Matrix (GLCM), SVM.
Scope of the Article: Machine Learning.