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\title{Benchmarking CNN and Cutting-Edge Transformer Models for Brain Tumor
Classification Through Transfer Learning}



\author[1]{Md Saiful Islam Sajol}%
\author[2]{A S M Jahid Hasan}%
\affil[1]{Dept. of CSE, Louisiana State University Louisiana}%
\affil[2]{Dept. of ECE North South University Dhaka}%


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  \date{April 29, 2026}


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\begin{abstract}
Brain tumor is a serious disease that can lead to fatal consequences.
Moreover, there are different types of brain tumor with different
progression rate and severeness. Thus, brain tumor classification is an
essential task in medical diagnosis. Convolutional Neural Network (CNN)
based deep learning methods shown great potential brain tumor
classification using Magnetic Resonance Imaging (MRI) scans of brain.
Recent development in transformer based deep learning models have shown
promising outcome in image classification task. However, these models
have not yet much explored in the application of brain tumor
classification. In this paper, we employ seven advanced models which
utilizes transformers or mimics the selfattention operation of
transformers through their design, for brain tumor classification. To
assess their performance for this particular task, five conventional CNN
based methods have also been applied and compared with these models. To
make our assessment more coherent and comprehensive we perform the
comparison for four different datasets. The results indicate that
transformer-based advanced models do not provide a distinct advantage
over conventional CNN-based models. The CNN-based ResNet-50 performs
well overall, especially with smaller datasets. For larger datasets,
transformer-based models generally perform better than CNN-based models,
although the difference is not significant.

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Cite as :%
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