MEHW‐SVM multi‐kernel approach for improved brain tumour classification

Abstract

Abstract The human brain, the primary constituent of the nervous system, exhibits distinctive complexities that present considerable difficulties for healthcare practitioners, specifically in categorizing brain tumours. Magnetic resonance imaging is a widely favoured imaging modality for detecting brain tumours due to its extensive range of image characteristics and utilization of non‐ionizing radiation. The primary objective of the current investigation is to differentiate between three distinct classifications of brain tumours by introducing a novel methodology. The utilization of a combined feature extraction technique that integrates novel global grey level co‐occurrence matrix and local binary patterns is employed, thereby offering a comprehensive representation of the structural and textural information contained within the images. Principal component analysis is used to improve the model's efficiency for effective feature selection and dimensionality reduction. This study presents a novel framework incorporating four separate kernel functions, Minkowski–Gaussian, exponential support vector machine (SVM), histogram intersection SVM, and wavelet kernel, into a SVM classifier. The ensemble kernel employed in this study is specifically designed to classify glioma, meningioma, and pituitary tumours. Its implementation enhances the model's robustness and adaptability, surpassing the performance of conventional single‐kernel SVM approaches. This study substantially contributes to medical image classification by utilizing innovative kernel functions and advanced machine‐learning techniques. The findings demonstrate the potential for enhanced diagnostic accuracy in brain tumour cases. The presented approach shows promise in effectively addressing the intricate challenges associated with classifying brain tumours.

Keywords

• brain tumours
• global grey level co‐occurrence matrix (GLCM)
• local binary patterns (LBP)
• principal component analysis (PCA)