Automatic detection of kidneys on abdominal CT images using Aggregate Channel Features
Küçük Resim Yok
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Accurate 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.
Açıklama
The IEEE Systems, Man, and Cybernetics Society (SMC)
16th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2022 -- 8 August 2022 through 12 August 2022 -- Biarritz -- 182947
16th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2022 -- 8 August 2022 through 12 August 2022 -- Biarritz -- 182947
Anahtar Kelimeler
Abdominal CT images, ACF object detection algorithm, automatic kidney detection
Kaynak
16th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2022
WoS Q Değeri
Scopus Q Değeri
N/A
