Tumor Detection by Classification of Brain MRI Images Using the Vision Transformers

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The interplay between applied mathematics and artificial intelligence is pivotal for advancing both fields. AI fundamentally relies on statistical and mathematical techniques to derive models from data, thus enabling computers to improve their performance over time. Classification of brain MRI images for tumor detection has improved significantly with the advent of machine learning and deep learning techniques. Classical classifiers such as Support Vector Machines (SVM), Tree, and k-Nearest Neighbors (k-NN) have been widely used in conjunction with feature extraction methods to improve the accuracy of tumor detection in MRI scans. Recent studies have shown that classical classifiers can effectively analyze features extracted from MRI images, which can lead to improved diagnostic capabilities. Feature extraction is a critical step in the classification process. Classification of brain MRI images using Vision Transformers (ViT) represents a significant advancement in medical imaging and tumor detection. ViT leverages the transformer architecture, which is highly successful in natural language processing, to effectively process visual data. This approach allows capturing long-range dependencies within images and enhances the ability of the model to distinguish complex patterns associated with brain tumors. Recent studies have demonstrated the effectiveness of ViT in various classification tasks, including medical imaging. In our study, the classification accuracy of the dataset from the ViT network was 78.26%. In order to increase tumor detection performance, features of ViT network were extracted and given to classical classifiers, and 81.9% accuracy was achieved in Tree classifier. As a result, classification of brain MRI images using ViT represents a new approach with the strengths of deep learning and traditional machine learning methods, namely feature extraction and classification in classical classifiers.