Analysis Of K Means Clustering And Classifiers In Diagnosing Abnormality Of The Ultrasonic Liver Images

Document Type : Primary Research paper


1 Assistant Professor, Electronics And Instrumentation Engineering, Bannari Amman Institute Of Technology, Sathyamangalam, India

2 Professor, Department Of Electronics And Communication Engineering, Bannari Amman Institute Of Technology, Sathyamangalam, India


This Paper Investigates The Diagnosis Of Liver Abnormalities From Ultrasonic
Images Using K Means Clustering Algorithm And Five Classifiers. Nowadays, Liver Cancer
Is One Of The Most Serious Health Problems. Medical Imaging Is Powerful Tool For
Diagnosing The Abnormalities In The Earlier Stage. The Features Are Extracted Using K
Means Clustering And Principal Component Analysis (PCA),Expectation Maximization
(EM),EM PCA, Kernel PCA,Gaussian Mixture Model (GMM) Classifier Are Used To Detect
The Liver Image As Normal Or Abnormal. The Parameters Such As Sensitivity, Specificity,
Accuracy, Precision, Error Rate, Mathew Correlation Coefficient (MCC), And Classifier
Success Index (CSI) Are Analyzed And Compared. When Compared With All The
Classifier The Logistic Regression Attained A Higher Accuracy Of 80.95%.


Volume 12, Issue 3 - Serial Number 3
ICMMNT-2021 International Virtual Conference on Materials, Manufacturing and Nanotechnology, 30th June, 2021.
June 2021
Pages 1589-1595