Abstract
Cancer of the lung is a principal cause of mortality due to cancer on a global scale. Traditional imaging techniques suffer from subjectivity limitations. Meanwhile, convolutional neural networks (CNNs) within deep learning, though highly effective in image classification, still have limitations when dealing with complex and data-scarce medical images. To address this challenge, this paper proposes a data-efficient image Transformer (DeiT) model based on the Transformer architecture with a self-attention mechanism, enhanced through knowledge distillation. This model can capture global information in images and improve the classification accuracy of lung cancer images under small-sample conditions by leveraging a teacher model. Through model training and evaluation, results demonstrate that the DeiT model achieves an impressive prediction accuracy of 99.96% under small-sample medical imaging conditions. This highlights the advantages of the Transformer architecture in medical image analysis. The findings provide a new perspective for early lung cancer detection and underscore the powerful performance of the DeiT model in handling complex small-sample data conditions.