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Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm




TekijätOzaltin Oznur, Coskun Orhan, Yeniay Ozgur, Subasi Abdulhamit

KustantajaWiley

Julkaisuvuosi2023

JournalInternational Journal of Imaging Systems and Technology

Tietokannassa oleva lehden nimiINTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY

Lehden akronyymiINT J IMAG SYST TECH

Vuosikerta33

Numero1

Aloitussivu69

Lopetussivu91

Sivujen määrä23

ISSN0899-9457

eISSN1098-1098

DOIhttps://doi.org/10.1002/ima.22806

Verkko-osoitehttps://onlinelibrary.wiley.com/doi/10.1002/ima.22806

Rinnakkaistallenteen osoitehttps://research.utu.fi/converis/portal/detail/Publication/176474709


Tiivistelmä
Classification of brain hemorrhage computed tomography (CT) images provides a better diagnostic implementation for emergency patients. Attentively, each brain CT image must be examined by doctors. This situation is time-consuming, exhausting, and sometimes leads to making errors. Hence, we aim to find the best algorithm owing to a requirement for automatic classification of CT images to detect brain hemorrhage. In this study, we developed OzNet hybrid algorithm, which is a novel convolution neural networks (CNN) algorithm. Although OzNet achieves high classification performance, we combine it with Neighborhood Component Analysis (NCA) and many classifiers: Artificial neural networks (ANN), Adaboost, Bagging, Decision Tree, K-Nearest Neighbor (K-NN), Linear Discriminant Analysis (LDA), Naive Bayes and Support Vector Machines (SVM). In addition, Oznet is utilized for feature extraction, where 4096 features are extracted from the fully connected layer. These features are reduced to have significant and informative features with minimum loss by NCA. Eventually, we use these classifiers to classify these significant features. Finally, experimental results display that OzNet-NCA-ANN excellent classifier model and achieves 100% accuracy with created Dataset 2 from Brain Hemorrhage CT images.

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Last updated on 2024-26-11 at 21:47