A Robust Algorithm for Classification and Diagnosis of Brain Disease Using Local Linear Approximation and Generalized Autoregressive Conditional Heteroscedasticity Model

Ali Hamzenejad, Saeid Jafarzadeh Ghoushchi, Vahid Baradaran and Abbas Mardani
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Ali Hamzenejad: Department of Industrial engineering, Islamic Azad University Tehran North Branch, Tehran 1477893855, Iran
Saeid Jafarzadeh Ghoushchi: Department of Industrial engineering, Urmia University of Technology, Urmia 5716693188, Iran
Vahid Baradaran: Department of Industrial engineering, Islamic Azad University Tehran North Branch, Tehran 1477893855, Iran
Abbas Mardani: Informetrics Research Group, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam

Mathematics, 2020, vol. 8, issue 8, 1-19

Abstract: Regions detection has an influence on the better treatment of brain tumors. Existing algorithms in the early detection of tumors are difficult to diagnose reliably. In this paper, we introduced a new robust algorithm using three methods for the classification of brain disease. The first method is Wavelet-Generalized Autoregressive Conditional Heteroscedasticity-K-Nearest Neighbor (W-GARCH-KNN). The Two-Dimensional Discrete Wavelet (2D-DWT) is utilized as the input images. The sub-banded wavelet coefficients are modeled using the GARCH model. The features of the GARCH model are considered as the main property vector. The second method is the Developed Wavelet-GARCH-KNN (D-WGK), which solves the incompatibility of the WGK method for the use of a low pass sub-band. The third method is the Wavelet Local Linear Approximation (LLA)-KNN, which we used for modeling the wavelet sub-bands. The extracted features were applied separately to determine the normal image or brain tumor based on classification methods. The classification was performed for the diagnosis of tumor types. The empirical results showed that the proposed algorithm obtained a high rate of classification and better practices than recently introduced algorithms while requiring a smaller number of classification features. According to the results, the Low-Low sub-bands are not adopted with the GARCH model; therefore, with the use of homomorphic filtering, this limitation is overcome. The results showed that the presented Local Linear (LL) method was better than the GARCH model for modeling wavelet sub-bands.

Keywords: Magnetic Resonance Imaging (MRI); wavelet transform; GARCH; LLA; LDA; KNN (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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