Brain Tumor Classification on MRI Images by using Classical Local Binary Patterns and Histograms of Oriented Gradients

Brain tumors pose significant threats within neurological disorders, demanding accurate classification for effective diagnosis and treatment. This study explores brain tumor classification employing Classical Local Binary Patterns (CLBP) and Convolutional Neural Networks (CNN), alongside texture feature extraction from MRI images using classical LBP and HOG (Histogram of Oriented Gradients). These methods adeptly capture both local and global texture patterns crucial for tumor identification. Our proposed framework encompasses three pivotal steps: image pre-processing, feature extraction via CLBP, and classification utilizing CNN. Evaluation on a publicly available brain tumor dataset showcased an impressive 95.6% accuracy in tumor classification, affirming the efficacy of the CLBP+CNN approach. This method bears promising implications for enhancing clinical diagnosis and treatment planning. Furthermore, we propose future extensions including CLBPs such as DLBP and LBP. DLBP introduces a parameter, ’D’, dictating pixel distance, while LBP varies pixel values across specified ranges. Additionally, tumor classification was explored employing ANN, AlDE, and LDA classification methods, with future prospects of incorporating DLBP, LBP, and CLBP extractions from MRI images within the dataset.