Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm
Subject
Neighborhood Component Analysis (NCA)Artificial Intelligence (AI)
Convolutional Neural Networks (CNN)
K-Nearest Neighbor (K-NN)
Linear Discriminant Analysis (LDA)
Support Vector Machines (SVM)
Naïve Bayes
Date
2023-01-12
Metadata
Show full item recordAbstract
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), Naïve 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.Department
Computer SciencePublisher
John Wiley & Sons, Inc.Journal title
International Journal of Imaging Systems and Technologyae974a485f413a2113503eed53cd6c53
https://doi.org/10.1002/ima.22806