Automatic detection of kidneys on abdominal CT images using Aggregate Channel Features

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dc.contributor.authorKaraman, Merve
dc.contributor.authorCinar, Salim
dc.date.accessioned2024-11-07T10:39:26Z
dc.date.available2024-11-07T10:39:26Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.descriptionThe IEEE Systems, Man, and Cybernetics Society (SMC)
dc.description16th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2022 -- 8 August 2022 through 12 August 2022 -- Biarritz -- 182947
dc.description.abstractAccurate detection of kidney regions in abdominal CT images makes it easier to detect formations such as cysts, lesions, and stones in the kidneys. In this study, the Aggregate Channel Features (ACF) algorithm, which is a machine learning method, is used for automatic detection of the kidneys. Negative samples are automatically taken from the images during the learning process. The ACF obtained are formed alternately and repeatedly for N steps using the AdaBoost classifier. At each step negative samples are removed and collected with the previous ones. The confusion matrix and k-fold cross-correlation methods are used to test the performance of the study. The data set fragmented according to k-fold is trained according to the location information of the labeled objects using the ACF. Recall, precision, and F1 scores gleaned from the confusion matrix are used in performance analysis. The results show that the proposed method can successfully detect kidney regions. © 2022 IEEE.
dc.identifier.doi10.1109/INISTA55318.2022.9894149
dc.identifier.isbn978-166549810-4
dc.identifier.scopus2-s2.0-85139595618
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/INISTA55318.2022.9894149
dc.identifier.urihttps://hdl.handle.net/11480/10956
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof16th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2022
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectAbdominal CT images
dc.subjectACF object detection algorithm
dc.subjectautomatic kidney detection
dc.titleAutomatic detection of kidneys on abdominal CT images using Aggregate Channel Features
dc.typeConference Object

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