A hyperdimensional framework: Unveiling the interplay of RBP and GSN within CNNs for ultra-precise brain tumor classification

Abstract

This study presents the RBP-CNN model, a convolutional neural network specifically designed for the precise classification of brain tumors in medical imaging. Conventional methods often encounter difficulties in extracting image noise and texture features, which has led to the incorporation of regional binary patterns (RBP) and Gray Standard Normalization (GSN) preprocessing techniques in CNN. The research addresses fundamental inquiries regarding the impact of the model on accuracy, false classifications, and efficiency. The novelty of RBP-CNN lies in its distinctive approach to extracting texture features, which involves optimizing pixel values through GSN preprocessing and generating regional binary patterns based on integral images. The objective of this research is to bridge a critical gap by providing a more accurate and efficient model for classifying brain tumors. The key findings reveal the exceptional performance of RBP-CNN, achieving a classification accuracy of 96% with a reduced false classification ratio of 7% across a dataset of 3000 samples. Comparative analyses position RBP-CNN as superior to alternative models in terms of accuracy, false classification rates, and efficiency. The structural insights and hyperparameter values of the model, as well as its application to the FigShare dataset, demonstrate its robustness and scalability. RBP-CNN emerges as an innovative and effective solution, advancing the field of medical image categorization. The findings of this study contribute a novel methodology, paving the way for future exploration in hyperspectral image applications and positioning RBP-CNN as a potential state-of-the-art tool for medical image analysis.