Comparative Analysis of Machine Learning Techniques on the BraTS Dataset for Brain Tumor Classification

In this study, machine learning techniques for identification of brain tumor were compared with the BraTS 2024 dataset. A variety of models included traditional machine learning algorithms such as Random Forest or more advanced deep learning architectures including Simple CNN, VGG16, VGG19, ResNet50, Inception-ResNetV2, and Efficient Net are investigated within the research. Preprocessing techniques were adopted to optimize the model performance on the dataset. The Random Forest algorithm gave the best result, with an accuracy of 87%, which was much better than the deep learning models, which had an accuracy between 47% and 70%. These findings have important applications for automated brain tumor diagnosis. They emphasize the criticality of the correct selection and tuning of the algorithm to improve the classification of tumor subtypes. First, this research shows that deep learning models are typically considered to be state of the art deep learning models for image analysis tasks, but in some cases traditional machine learning methods such as random forest might still achieve better results than the most complex of neural networks. This delineates the importance of a fine-grained approach to model selection, with regard for details of the dataset as well as computational constraints and particular diagnostic requirements. The aim of the study is to improve patient outcome for more accurate and efficient brain tumor identification by refinement and optimization of these automated diagnosis systems.