Version 1
: Received: 10 April 2024 / Approved: 10 April 2024 / Online: 10 April 2024 (13:23:06 CEST)
How to cite:
Rajaguru, H.; R., K. Exploitation of Bio Inspired Classifiers for Performance Enhancement in Liver Cirrhosis Detection from Ultrasonic Images. Preprints2024, 2024040741. https://doi.org/10.20944/preprints202404.0741.v1
Rajaguru, H.; R., K. Exploitation of Bio Inspired Classifiers for Performance Enhancement in Liver Cirrhosis Detection from Ultrasonic Images. Preprints 2024, 2024040741. https://doi.org/10.20944/preprints202404.0741.v1
Rajaguru, H.; R., K. Exploitation of Bio Inspired Classifiers for Performance Enhancement in Liver Cirrhosis Detection from Ultrasonic Images. Preprints2024, 2024040741. https://doi.org/10.20944/preprints202404.0741.v1
APA Style
Rajaguru, H., & R., K. (2024). Exploitation of Bio Inspired Classifiers for Performance Enhancement in Liver Cirrhosis Detection from Ultrasonic Images. Preprints. https://doi.org/10.20944/preprints202404.0741.v1
Chicago/Turabian Style
Rajaguru, H. and Karthikamani R.. 2024 "Exploitation of Bio Inspired Classifiers for Performance Enhancement in Liver Cirrhosis Detection from Ultrasonic Images" Preprints. https://doi.org/10.20944/preprints202404.0741.v1
Abstract
In the current scenario, liver abnormalities are one of the most serious public health concerns. Cirrhosis of the liver is one of the foremost causes of demise from liver diseases. To accurately predict the status of liver cirrhosis, physicians frequently use automated computer-aided approaches. In this paper the clustering techniques like Fuzzy C Means (FCM), Possibilistic Fuzzy C Means (PFCM), Possibilistic C Means (PCM), Sample Entropy features are extracted from normal and cirrhotic liver ultrasonic Images. The extracted features are classified as normal and Cirrhosis one through Gaussian Mixture Model(GMM), Softmax Dis(SDC), Harmonic Search Algorithm (HSA), SVM(Linear), SVM(RBF), SVM(Polynomial), Artificial Algae Optimization (AAO) and hybrid classifier Artificial Algae Optimization (AAO) with Gaussian Mixture Mode (GMM). The classifiers performances are compared based on Accuracy, F1 Score, MCC, F Measure, Error rate and Jaccard metric (JM).The hybrid classifier AAO GMM with PFCM feature outperforms than other classifiers and attained an accuracy of 99.03 % with MCC of 0.90
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
