SwinBTC: Transfer Learning to Brain Tumor Classification for Healthcare Electronics Using Augmented MR Images
Honghao Gao, Yaping Wan, Hongxia Xu, Lingchao Chen, Junsheng Xiao, Qionghuizi Ran
Abstract
Brain tumors require AI-assisted, precise treatments. Methods based on healthcare electronics, such as magnetic resonance imaging (MRI), computed tomography (CT), and gastrointestinal endoscopy, are widely used in hospitals. Maximizing the functionality of these electronic devices is crucial for improving tumor lesion detection and is a challenge in AI-assisted medical applications. Precise classification is vital for effectively planning brain tumor treatments, and accurate classification results offer crucial insights that enable physicians to devise optimal treatment strategies. Therefore, this paper proposes a novel brain tumor classification method named SwinBTC, which is based on the healthcare Internet of Things (HIoT) and integrates a pretrained and fine-tuned Swin transformer model to improve the performance of healthcare electronics. First, a transfer learning (TL)-based brain tumor classification model called SwinBTC is proposed. The general features in the images are used to improve the classification ability of the brain tumor MRI results, further accelerate the model training speed, and avoid overfitting problems. Second, clinical magnetic resonance images obtained from HIoT nodes are used to enrich the dataset, and online and offline data augmentation techniques are used to expand the utilized dataset and increase data diversity, improving the generalizability and reliability of the developed model. Finally, the performance of the proposed approach is evaluated on the CE-MRI and TT-MRI datasets via various classification metrics, and the experimental results reveal that our method outperforms other baselines.