Multiclass Classification of Brain Tumors with Various Deep Learning Models †

Abstract

Brain cancer is one of the most dangerous cancer types in the world, and thousands of people are suffering from malignant brain tumors. Depending on the level of cancer, early diagnosis can be a lifesaver. However, thousands of scans must be studied in order to classify tumor types with high accuracy. Deep learning models can handle that amount of data, and they can present results with high accuracy. It is already known that deep learning models can give different results depending on the dataset. In this paper, the effectiveness of some of the deep learning models on two different publicly available MRI (Magnetic Resonance Imaging) brain tumor datasets is examined. The reason for choosing this topic is that we are trying to find the best solution to classify tumors in the datasets. Different deep learning models are used separately on preprocessed datasets with the Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing variable to extract features from images and classify them. Datasets are shuffled randomly for 80% training, 10% validation, and 10% testing. For fine-tuning, models are modified so that the output channel of the classifier is equal to the number of classes in the datasets. The results show that pre-trained and fine-tuned ResNet, RegNet, and Vision Transformer (ViT) deep learning models can achieve accuracies higher than 90% and that they can be used as classifiers when diagnosis is required.