TY - JOUR ID - 95270 TI - Classification of Brain Tumor Using Sparse Non-negative Matrix Factorization and Structured Sparse Principal Component Analysis JO - Journal of Machine Vision and Image Processing JA - JMVIP LA - en SN - AU - Mavaddati, Samira AD - Electronic Department, Faculty of Engineering and Technology, University of Mazandaran, Babolsar, Iran Y1 - 2020 PY - 2020 VL - 7 IS - 1 SP - 77 EP - 91 KW - Classification of brain tumor KW - Sparse non-negative matrix factorization KW - Sparse structured principal component analysis KW - Statistical-based feature KW - Texture-based feature DO - N2 - 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 non-negative matrix factorization algorithm is used to learn the over-complete models based on the characteristics of each data category. Also, sparse structured principal component analysis algorithm is applied to reduce the dimension of training data. 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. UR - https://jmvip.sinaweb.net/article_95270.html L1 - https://jmvip.sinaweb.net/article_95270_10c0d20713b647eb2e3995de3407ccb7.pdf ER -