A Comprehensive Approach to Brain Tumor Classification in MRI: Unifying Classical Local Binary Patterns and Convolution Neural Networks

Abstract

Brain tumors, among the most perilous neurological disorders affecting the human nervous system, are categorized into glioma, meningioma, and Pituitary types. The proposed system combines Classical Local Binary Patterns (CLBP), Histogram of Oriented Gradients (HOG), and Convolutional Neural Networks (CNN) to extract texture information from MRI images. The methodology comprises three key steps: image pre-processing, feature extraction, and classification. By evaluating a publicly accessible brain tumor dataset, the proposed approach attains an impressive accuracy of 96%. The findings highlight the potential uses of the CLBP+CNN approach in clinical diagnosis and treatment planning, demonstrating the possibility of accurate and efficient brain tumor classification. The proposal introduces future extension methods like CLBPs (DLBP, ӨLBP), where the DLBP approach incorporates a 'D' parameter specifying the distance between neighboring pixels. The ӨLBP Method evaluates the pixel value by changing the 'Ө' value, i.e., 150, 450, 900 and 1200. The classification of tumors involved the application of various classification methods, namely ANN (Artificial Neural Networks), AlDE (Ant Lion Optimizer Differential Evolution), and LDA (Linear Discriminant Analysis). This classification is executed using feature extractions derived from CLBP (DLBP & ӨLBP) applied to MRI images within the dataset.