Volume 19, Issue 2 (JIAEEE Vol.19 No.2 2022)
Journal of Iranian Association of Electrical and Electronics Engineers 2022, 19(2): 177-188 |
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Mavaddati S. Classification of Brain Tumor Using Model Learning Based on Statistical and Texture Features. Journal of Iranian Association of Electrical and Electronics Engineers 2022; 19 (2) :177-188
URL: http://jiaeee.com/article-1-673-en.html
URL: http://jiaeee.com/article-1-673-en.html
University of Mazandaran
Abstract: (1033 Views)
Classification of brain tumors using MRI images along with medical knowledge can lead to proper decision-making on the patient's condition. Also, classification of benign or malignant tumors is one of the challenging issues due to the need for detailed analysis of tumor tissue. Therefore, addressing this field using image processing techniques can be very important. In this paper, various types of texture-based and statistical-based features are used to determine the type of brain tumor and different types of features are applied in this classification procedure. Sparse coding and dictionary learning techniques are used to learn the over-complete models based on the characteristics of each data category. The classification process is carried out based on the calculated energy of the sparse coefficients. Also, the results of this categorization are compared with the results of the classification based on the neural network and support vector machine. The simulation results show that the proposed method based on the selected combinational features and learning the over-complete dictionaries can be able to classify the types of brain tumors precisely.
Keywords: Classification, Brain tumor, Model Learning, Sparse representation, Statistical-based feature, Texture-based feature.
Type of Article: Research |
Subject:
Electronic
Received: 2018/09/23 | Accepted: 2020/04/4 | Published: 2022/08/31
Received: 2018/09/23 | Accepted: 2020/04/4 | Published: 2022/08/31
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