Multi-Class Brain Tumour Classification with CNN on MRI Scans

The process of classifying brain tumors using MRI data is difficult to do accurately. The challenges and limitations of manual analysis in treatments can be overcome by implementing Computer-Aided Diagnosis (CAD) systems for categorizing brain tumors. This study assesses the performance of five well-known Transfer Learning CNN architectures - VGG19, VGG16, GoogleNet, ResNet-50, and DenseNet-121 - using magnetic resonance imaging (MRI) scans. The main goal is to classify brain malignancies into four groups: no tumor, pituitary, meningioma, and glioma. The focus of this research for the classification task was on data preparation, training on preprocessed data, and comparing each model using evaluation metrics such as f1-score, accuracy, recall, and precision. Following preprocessing and training, the evaluation of the five CNN models revealed promising results for the VGG models, with both VGG19 and VGG16 achieving an accuracy score of 97%. This study indicates that among the base variants of Transfer Learning models, VGG19 and VGG16 are well-suited for classifying different types of brain tumors.