Advancing brain tumor diagnosis: Deep siamese convolutional neural network as a superior model for MRI classification

Abstract

The timely detection and precise classification of brain tumors using techniques such as magnetic resonance imaging (MRI) are imperative for optimizing treatment strategies and improving patient outcomes. This study evaluated five state-of-the-art classification models to determine the optimal model for brain tumor classification and diagnosis using MRI. We utilized 3064 T1-weighted contrast-enhanced brain MRI images that included gliomas, pituitary tumors, and meningiomas. Our analysis employed five advanced classification model categories: machine learning classifiers, deep learning-based pre-trained models, convolutional neural networks (CNNs), hyperparameter-tuned deep CNNs, and deep siamese CNNs (DeepSCNNs). The performance of these models was assessed using several metrics, such as accuracy, precision, sensitivity, recall, and F1-score, to ensure a comprehensive evaluation of their classification capabilities. DeepSCNN exhibited remarkable classification performance, attaining exceptional precision and recall values, with an overall F1-score of 0.96. DeepSCNN consistently outperformed the other models in terms of F1-score and robustness, setting a new standard for brain tumor classification. The superior accuracy of DeepSCNN across all classification tasks underscores its potential as a tool for precise and efficient brain tumor classification. This advance may significantly contribute to improved patient outcomes in neuro-oncology diagnostics, offering insight and guidance for future studies.