Capuchin Red Kite–optimized Swin Transformer-based Convolutional Block Attention Module for Early Diagnosis and Classification of Pneumonia.

INTRODUCTION

Pneumonia is a serious respiratory disease that requires early and precise diagnosis to reduce morbidity and mortality. This study aims to develop an efficient deep learning model for the accurate classification of pneumonia, COVID-19, and normal cases using chest X-ray and CT images.

METHODS

The proposed model combines Capuchin Red Kite Optimization (CRKO) with a Swin Transformer–based Convolutional Block Attention Module (ST-CBAM). A Butterworth filter is applied during preprocessing to enhance image quality. ResNet and Vision Transformer are used for feature extraction, capturing local and global patterns, respectively. These features are fused using Adaptive Gated Recurrent Units (AGRU) and optimized with CRKO. The model is trained and validated using a publicly available chest X-ray dataset from Kaggle.

RESULTS

The model achieved classification accuracies of 99% for normal, 99.9% for COVID-19, and 98.2% for pneumonia cases. It recorded an AUC of 98.93%, outperforming existing models such as ACNN, 3D-CNN, LWHNN, and CA-DCNN in both accuracy and execution time.

DISCUSSION: The integration of CRKO with ST-CBAM, along with hybrid feature extraction and fusion techniques, contributes to the model’s high performance. The results indicate a strong potential for clinical application. However, future studies should validate the model across diverse, realworld datasets to ensure generalizability.

CONCLUSION

The proposed deep learning framework offers a fast, accurate, and reliable solution for automated pneumonia diagnosis, showing promise for deployment in medical imaging systems.