Brain tumor detection and classification are critical tasks in medical image analysis, particularly in early-stage diagnosis, where accurate and timely detection can significantly improve treatment outcomes. In this study, we apply various statistical and machine learning models to detect and classify brain tumors using brain MRI images. We explore a variety of statistical models including linear, logistic, and Bayesian regressions, and the machine learning models including decision tree, random forest, single-layer perceptron, multi-layer perceptron, convolutional neural network (CNN), recurrent neural network, and long short-term memory. Our findings show that CNN outperforms other models, achieving the best performance. Additionally, we confirm that the CNN model can also work for multi-class classification, distinguishing between four categories of brain MRI images such as normal, glioma, meningioma, and pituitary tumor images. This study demonstrates that machine learning approaches are suitable for brain tumor detection and classification, facilitating real-world medical applications in assisting radiologists with early and accurate diagnosis.

本研究解决了脑肿瘤检测与分类中的准确性与时效性问题，旨在改善早期诊断的效果。通过应用多种统计和机器学习模型，尤其是卷积神经网络（CNN），研究发现CNN在多类别脑MRI图像分类中表现最佳，具有显著的医疗应用潜力，能够支持放射科医师进行早期准确的诊断。

利用机器学习进行脑肿瘤检测与分类