Pneumonia, a severe respiratory disease, poses significant diagnostic challenges, especially in underdeveloped regions. Traditional diagnostic methods, such as chest X-rays, suffer from variability in interpretation among radiologists, necessitating reliable automated tools. In this study, we propose a novel approach combining deep learning and transformer-based attention mechanisms to enhance pneumonia detection from chest X-rays. Our method begins with lung segmentation using a TransUNet model that integrates our specialized transformer module, which has fewer parameters compared to common transformers while maintaining performance. This model is trained on the "Chest Xray Masks and Labels" dataset and then applied to the Kermany and Cohen datasets to isolate lung regions, enhancing subsequent classification tasks. For classification, we employ pre-trained ResNet models (ResNet-50 and ResNet-101) to extract multi-scale feature maps, processed through our modified transformer module. By employing our specialized transformer, we attain superior results with significantly fewer parameters compared to common transformer models. Our approach achieves high accuracy rates of 92.79% on the Kermany dataset and 95.11% on the Cohen dataset, ensuring robust and efficient performance suitable for resource-constrained environments. "https://github.com/amirrezafateh/Multi-Scale-Transformer-Pneumonia"

本研究针对传统肺炎诊断方法在低开发地区面临的挑战，提出了一种结合深度学习和变换器注意机制的新方法，以提高胸部X光片的肺炎检测性能。研究表明，该方法在Kermany和Cohen数据集上实现了高达92.79%和95.11%的准确率，为资源有限的环境提供了一种可靠而高效的检测工具。

基于新型多尺度变换器的高效准确肺炎检测