Document Type
Article
Source of Publication
Scientific Reports
Publication Date
12-1-2025
Abstract
The precise detection and localization of abnormalities in radiological images are very crucial for clinical diagnosis and treatment planning. To build reliable models, large and annotated datasets are required that contain disease labels and abnormality locations. Most of the time, radiologists face challenges in identifying and segmenting thoracic diseases such as COVID-19, Pneumonia, Tuberculosis, and lung cancer due to overlapping visual patterns in X-ray images. This study proposes a dual-model approach: Gated Vision Transformers (GViT) for classification and Swin Transformer V2 for segmentation and localization. GViT successfully identifies thoracic diseases that exhibit similar radiographic features, while Swin Transformer V2 maps lung areas and pinpoints affected regions. Classification metrics, including precision, recall, and F1-scores, surpassed 0.95 while the Intersection over Union (IoU) score reached 90.98%. Performance assessment via Dice Coefficient, Boundary F1-Score, and Hausdorff Distance demonstrated the system’s excellent effectiveness. This artificial intelligence solution will help radiologists in decreasing their mental workload while improving diagnostic precision in healthcare systems that face resource constraints. Transformer-based architectures show strong promise for enhancing medical imaging procedures, according to the study results. Future AI tools should build on this foundation, focusing on comprehensive and precise detection of chest diseases to support effective clinical decision-making.
DOI Link
ISSN
Publisher
Springer Science and Business Media LLC
Volume
15
Issue
1
Disciplines
Computer Sciences
Keywords
And lung cancer detection, Artificial intelligence in radiology, Chest disease detection, Gated vision transformer, Swin transformer V2
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Ahmad, Kamal; Rehman, Hafeez Ur; Shah, Babar; Ali, Farman; and Hussain, Irfan, "Dual-model approach for accurate chest disease detection using GViT and swin transformer V2" (2025). All Works. 7505.
https://zuscholars.zu.ac.ae/works/7505
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series