  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"
