Author(s):
Shyamala Devi M, Sruthi A. N, Saranya Jothi C
Email(s):
shyamalapmr@gmail.com
DOI:
10.5958/0974-360X.2018.00080.X 
Address:
Shyamala Devi M*, Sruthi A. N, Saranya Jothi C
Associate Professor, Assistant Professor, Department of Department of Computer Science and Engineering,
Vel Tech Rangarajan Dr.Saguntahala R and D Institute of Science and Technology, Avadi, Chennai, TN, India.
*Corresponding Author
Published In:
Volume - 11,
Issue - 2,
Year - 2018
ABSTRACT:
The survival rate of Liver tumor patients can be improved if we perform early detection and treating them. The clinical researches have exposed that the volume measurement can give the best reflection of the tumor response. The liver tumor requires the tumor segmentation. This paper proposes an automatic support system for stage classification using artificial neural network (learning machine) and to detect Liver Tumor through fuzzy clustering methods for medical application. The detection of the Liver Tumor is a challenging problem, due to the structure of the Tumor cells. This project presents a segmentation method, fuzzy clustering algorithm, for segmenting Magnetic Resonance images to detect the Liver Tumor in its early stages and to analyze anatomical structures.
Cite this article:
Shyamala Devi M, Sruthi A. N, Saranya Jothi C. MRI Liver Tumor Classification Using Machine Learning Approach and Structure Analysis. Research J. Pharm. and Tech 2018; 11(2):434-438. doi: 10.5958/0974-360X.2018.00080.X
Cite(Electronic):
Shyamala Devi M, Sruthi A. N, Saranya Jothi C. MRI Liver Tumor Classification Using Machine Learning Approach and Structure Analysis. Research J. Pharm. and Tech 2018; 11(2):434-438. doi: 10.5958/0974-360X.2018.00080.X Available on: https://rjptonline.org/AbstractView.aspx?PID=2018-11-2-2