Comparison of ML methods for brain tumor classification based on Kaggle dataset.
Dataset: https://www.kaggle.com/datasets/fernando2rad/brain-tumor-mri-images-44c
Lately, machine learning has shown huge potential for medical imaging technologies as the so-called artificial neural networks outperforms other traditional methods. In this project, we would like to focus on computer vision and demonstrate how different machine learning methods can classify 44 types of brain tumors (for e.x astrocytoma, neurocytoma, and tuberculoma) shown in MRI images. Particularly, we want to compare different machine learning models ranging from classical ones (e.g Support Vector Machine, Naive Bayes) to deep neural networks of supervised learning (e.g. ResNet, VGG) and unsupervised learning (e.g. VAE, GAN).
Nguyen .K, Nguyen .H, Amirreza .A