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Original research

MRI brain image classification using Linear Vector Quantization Classifier

* Автор, отвечающий за переписку

[email protected]

Abstract

The metastases cancer other than the lifestyle-related or environmental related no known facts for the brain tumors. Only factors that may cause brain tumors might be the exposure to high ionizing radiation and a family history of any brain disease also increase brain cancer risk. The cancerous brain is a brain disorder that shapes masses in cells called tumors. The early diagnosis of brain cancer using the Magnetic Resonance Imaging (MRI) scan image for cancer disease is required to reduce the mortality rate. Dual-Tree Mband Wavelet Transform (DTMBWT) based feature extraction, and Linear Vector Quantization Classifier (LVQC) based MRI brain image classification. DTMBWT decomposes the MRI brain images in the frequency domain as the sub-bands for fuzzy-based low and high components to evaluate the features selected. The Sub-band Energy Features (SEF) for individual and sub-set ranking helps classify normal and abnormal images that LVQC for output prediction characterizes. The results show the classification accuracy of 95% using DTMBWT based SEF and LVQC classifiers.

Imprint

A. Ratna Raju, Suresh Pabboju, R. Rajeswara Rao. MRI brain image classification using Linear Vector Quantization Classifier. Cardiometry; Issue 22; May 2022; p.516-519; DOI: 10.18137/cardiometry.2022.22.516519; Available from: https://www.cardiometry.net/issues/no22-may-2022/MRI_brain_image

Ключевые слова

MRI,  Dual-Tree M-band Wavelet Transform,  Sub-band,  Energy features,  Linear Vector Quantization
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