Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kausar, Fizhan; Ramamurthy, B. Title A name given to the resource Coati Optimization Algorithm for Detecting Pediatric Kidney Abnormalities using Ultrasound Images Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Research Journal of Multidisciplinary Scope;Volume;6;Issue;2;pp.874-884 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.47857/irjms.2025.v06i02.03236" target="_blank" rel="noreferrer noopener">https://doi.org/10.47857/irjms.2025.v06i02.03236</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105005617086?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105005617086?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kausar F., CHRIST University, Bangalore, India; Ramamurthy B., CHRIST University, Bangalore, India Description An account of the resource This study aimed to classify pediatric ultrasound images as normal or abnormal by identifying the optimal number of image texture features for analysis and developing an effective classification system using selected features. The experiment identified a successful feature selection and classification algorithm with a good performance. This study introduced a new approach for computer-assisted ultrasound image classification. Initially, a Gaussian median filter enhances the image quality and removes noise. For feature extraction, various features, including first-order derivatives, Gray Level Co-Occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Dependence Matrix (GLDM), Gray Level Size Matrix (GLSZM), and Neighbouring gray tone difference matrix (NGTDM), were extracted using the Pyrandiomics Python package. The Coati optimization algorithm (COA) was employed as a feature selection technique. The Classification was performed using Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), K-nearest Neighbor (KNN), Nae Bayes (NB), and Extreme Gradient Boosting (XG-Boost) algorithms. Therefore, this study proposed a new machine learning classifier, the Extreme Gradient Neighborhood classifier (XGNC), using NB, KNN, and XG-Boost, with a classification accuracy of 97.91%, which outperformed the other classifiers mentioned in the study. The results indicated that the optimal feature selection and classifier choice yielded the most accurate computer-aided diagnosis of kidney abnormalities. 2025, Iquz Galaxy Publisher. All rights reserved. Subject The topic of the resource Coati Optimization Algorithm (Coa); Extreme Gradient Neighborhood (Xgnc); Feature Selection; Kidney; Machine Learning (Ml); Ultrasound (Us) Publisher An entity responsible for making the resource available Iquz Galaxy Publisher Relation A related resource ISSN: 2582631X; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online