Hybrid Deep Learning Models for Segmentation and Classification of MRI Images of Brain Tumor by Integrating CNNs and Transformers

Abstract

The detection, segmentation, and classification of brain tumors are important challenges in medical imaging. These tasks require accurate and efficient methods to help clinicians make timely and precise diagnoses. The complex structure of brain tumors, along with their varied sizes, shapes, and locations, creates significant difficulties for automated analysis. Traditional imaging analysis methods, while useful, often suffer from limited accuracy, low reproducibility, and high dependency on expert knowledge, thus necessitating the development of advanced computational techniques. This motivated significant research into automated computer-based methods, especially deep learning models, which are being used more and more for the segmentation and classification of brain tumors. Traditional deep learning methods, such as CNNs and U-Net architectures, have shown good results in brain tumor segmentation and transfer learning for classification. Still, certain obstacles must be overcome: capturing long-range dependencies, modeling diverse tumor complexities, and modeling the lack of robustness in datasets. This research addresses these shortcomings by developing hybrid deep learning models that use CNNs, transformers, and attention for the segmentation and transfer learning, and integrated attentional processes for the classification of brain tumor. For the segmentation task, this study initially improved the baseline U-Net model by adding residual connections and attention processes. This improvement led to better accuracy and more reliable results. The proposed ARU-Net achieved a high Dice coefficient, remarkably outperforming the standard U-Net result. Regular CNN and U-Net-based models are good at finding local spatial patterns, but they don t take into account long-range dependencies, which makes them less useful on tumor boundaries that aren t straight and tissues that aren t uniform. Remarkably, hybrid CNN transformer models were designed that reflected even superior accuracy.