{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/efficient-and-accurate-pneumonia-detection","title":"Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer Approach","arxiv_id":"2408.04290","date":"2024-08-08","proceeding":null,"authors":["Alireza Saber","Pouria Parhami","Alimohammad Siahkarzadeh","Mansoor Fateh","Amirreza Fateh"],"abstract":"Pneumonia, a prevalent respiratory infection, remains a leading cause of morbidity and mortality worldwide, particularly among vulnerable populations. Chest X-rays serve as a primary tool for pneumonia detection; however, variations in imaging conditions and subtle visual indicators complicate consistent interpretation. Automated tools can enhance traditional methods by improving diagnostic reliability and supporting clinical decision-making. In this study, we propose a novel multi-scale transformer approach for pneumonia detection that integrates lung segmentation and classification into a unified framework. Our method introduces a lightweight transformer-enhanced TransUNet for precise lung segmentation, achieving a Dice score of 95.68% on the \"Chest X-ray Masks and Labels\" dataset with fewer parameters than traditional transformers. For classification, we employ pre-trained ResNet models (ResNet-50 and ResNet-101) to extract multi-scale feature maps, which are then processed through a modified transformer module to enhance pneumonia detection. This integration of multi-scale feature extraction and lightweight transformer modules ensures robust performance, making our method suitable for resource-constrained clinical environments. Our approach achieves 93.75% accuracy on the \"Kermany\" dataset and 96.04% accuracy on the \"Cohen\" dataset, outperforming existing methods while maintaining computational efficiency. This work demonstrates the potential of multi-scale transformer architectures to improve pneumonia diagnosis, offering a scalable and accurate solution to global healthcare challenges.\"https://github.com/amirrezafateh/Multi-Scale-Transformer-Pneumonia\"","url_abs":"https://arxiv.org/abs/2408.04290v4","url_pdf":"https://arxiv.org/pdf/2408.04290v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"efficient-and-accurate-pneumonia-detection","repo_url":"https://github.com/amirrezafateh/multi-scale-transformer-pneumonia","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"pneumonia-detection","task_name":"Pneumonia Detection"}],"methods":[{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/classification-on-covid-19-image-data","task":"Classification","dataset":"COVID-19 Image Data Collection","model":"MSTP","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"95.11"},"uses_additional_data":false},{"leaderboard":"/sota/pneumonia-detection-on-chest-x-ray-images-1","task":"Pneumonia Detection","dataset":"Chest X-ray images","model":"MSTP","rank_in_archive_order":4,"of":4,"metrics":{"Accuracy":"92.79"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}