Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm

ÖZALTIN Ö., Coskun O., YENİAY M. Ö., Subasi A.

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, cilt.33, ss.69-91, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 33
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1002/ima.22806
  • Dergi Adı: INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, INSPEC
  • Sayfa Sayıları: ss.69-91
  • Anahtar Kelimeler: classification, CNN, feature extraction, machine learning, NCA, OZNET, CONVOLUTIONAL NEURAL-NETWORK, INTRACEREBRAL HEMORRHAGE, INTRACRANIAL HEMORRHAGE, FEATURE-SELECTION, CT, SEGMENTATION, MANAGEMENT
  • Hacettepe Üniversitesi Adresli: Evet

Özet

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.