Classification of Brain Tumor Using Sparse Non-negative Matrix Factorization and Structured Sparse Principal Component Analysis

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Mavaddati Samira

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Journal: MACHINE VISION AND IMAGE PROCESSING Year:2020 | Volume:7 | Issue:1 Page(s): 77-91

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Information Journal Paper

Title

Classification of Brain Tumor Using Sparse Non-negative Matrix Factorization and Structured Sparse Principal Component Analysis

Author(s)

Mavaddati Samira | Issue Writer Certificate

Keywords

Classification of brain tumor

Sparse non-negative matrix factorization

Sparse structured principal component analysis

Statistical-based feature

Texture-based feature

Abstract

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.

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APA: Copy

Mavaddati, Samira. (2020). Classification of Brain Tumor Using Sparse Non-negative Matrix Factorization and Structured Sparse Principal Component Analysis. MACHINE VISION AND IMAGE PROCESSING, 7(1 ), 77-91. SID. https://sid.ir/paper/363502/en

Vancouver: Copy

Mavaddati Samira. Classification of Brain Tumor Using Sparse Non-negative Matrix Factorization and Structured Sparse Principal Component Analysis. MACHINE VISION AND IMAGE PROCESSING[Internet]. 2020;7(1 ):77-91. Available from: https://sid.ir/paper/363502/en

IEEE: Copy

Samira Mavaddati, “Classification of Brain Tumor Using Sparse Non-negative Matrix Factorization and Structured Sparse Principal Component Analysis,” MACHINE VISION AND IMAGE PROCESSING, vol. 7, no. 1 , pp. 77–91, 2020, [Online]. Available: https://sid.ir/paper/363502/en

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