Machine Learning in Brain Tumor Diagnosis: Assessing the Efficacy of Diverse Methods

Background: Brain tumors constitute a critical and potentially life-threatening condition that requires accurate and timely diagnosis. In recent years, machine learning (ML) approaches have emerged as powerful tools for assisting radiologists and clinicians in identifying and classifying tumors on medical imaging. While numerous ML methods, including conventional machine learning algorithms and deep learning-based models, have been explored, a comprehensive analysis of their efficacy in detecting, segmenting, and classifying brain tumors remains essential. Methods: We conducted a systematic evaluation of diverse ML methods used in brain tumor diagnosis. We first identified and collated relevant studies from major scientific databases, focusing on image-based applications such as magnetic resonance imaging (MRI). The methods included pre-processing pipelines, feature extraction techniques, and both supervised and unsupervised learning approaches. We then compared these methods based on standard metrics, including accuracy, sensitivity, specificity, and F1-score, to gain insights into their relative performance. Furthermore, we applied representative algorithms (such as support vector machines, convolutional neural networks, and random forests) to an open-access brain tumor imaging dataset to evaluate their performance and compare empirical findings. Results: Results indicated that deep learning models, particularly convolutional neural networks, consistently outperformed traditional ML models across various performance metrics. In particular, automated feature extraction in deep learning approaches proved pivotal in capturing nuanced information such as tumor shape and heterogeneity. Conventional algorithms still demonstrated merit when dataset sizes were limited or when computational resources were constrained. Additionally, various data augmentation techniques improved the robustness and generalizability of ML models in situations with scarce annotated data. Conclusion: Our findings suggest that deep learning-based methods hold the greatest potential for accurate and efficient brain tumor diagnosis, particularly when coupled with robust datasets and high-quality imaging. Nonetheless, careful selection of model architecture, training strategies, and validation protocols remains paramount to ensure reliable clinical translation.