Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer Approach

• URL: http://arxiv.org/abs/2408.04290v3
• Date: Sun, 26 Jan 2025 17:04:30 GMT
• Title: Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer Approach
• Authors: Alireza Saber, Pouria Parhami, Alimohammad Siahkarzadeh, Mansoor Fateh, Amirreza Fateh,
• Abstract summary: We propose a novel multi-scale transformer approach for pneumonia detection.<n>Our method integrates lung segmentation and classification into a unified framework.<n>Our approach achieves 93.75% accuracy on the "Kermany" dataset and 96.04% accuracy on the "Cohen" dataset.
• Score: 1.2233362977312945
• License: http://creativecommons.org/licenses/by/4.0/
• 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"

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