Open AccessPerformance Analysis of classifiers in Detection of Abnormalities from Ultrasonic Images-A Review
Author(s) -
R. Harikumar,
R. Karthikamani
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1084/1/012030
Subject(s) - support vector machine , artificial intelligence , pattern recognition (psychology) , classifier (uml) , computer science , artificial neural network , k nearest neighbors algorithm , machine learning
In this paper the ultrasonic abdominal diseases are classified using various classifiers and their performances are analysed. Early diagnosis of abdominal disease is very important in deciding the proper treatment process. To carry out any research work without the comparison of the proposed one with the other already existing method is not effective to give better result. Objective of this paper is to review the classification methods based on the standard parameters like, Sensitivity, Specificity, Accuracy. In this review four classification algorithms Naive Bays Classifier, Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Artificial Neural Networks(ANN) are compared.The SVM classifier attained higher accuracy of 98.33 % when compared with all the classifiers.