Volume 11, Issue 2 (summer 2025)
JMIS 2025, 11(2): 201-218 |
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Ethics code: IR.IAU.BIRJAND.REC.1404.036
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Ghasemi M, Ghaffari H R. Intelligent Detection of Subtle Pulmonary Abnormalities in CXR Images Using YOLO-CA-NET Framework Based on YOLOv11 and Coordinate Attention. JMIS 2025; 11 (2) :201-218
URL: http://jmis.hums.ac.ir/article-1-634-en.html
URL: http://jmis.hums.ac.ir/article-1-634-en.html
Department of Computer Engineering, Fe.C., Islamic Azad University, Ferdows, Iran
Abstract: (351 Views)
Introduction: Chest radiography is one of the vital tools in disease diagnosis; however, due to the subtlety of imaging findings and their overlap with complex anatomical structures, it is always susceptible to human error. The objective of this research is to present a new framework called YOLO-CA-NET to improve the detection accuracy and localization of subtle and small abnormalities in radiographic images using advanced attention mechanisms.
Methods: In this study, the YOLO-v1.1 model was upgraded by strategically integrating the Coordinate Attention module into its backbone blocks to extract position-sensitive information and preserve the spatial details of small lesions. For training and evaluation, the standard VdNv-CXR dataset, comprising 15,000 radiographic images with annotations for 14 common types of abnormalities, was utilized. Pre-processing included normalization and the CLAHE technique to enhance the contrast of low-visibility findings.
Results: The research findings indicated that the proposed YOLO-CA-NET model achieved a mean Average Precision (mAP) of 0.329 and a mean Recall of 0.514. These results demonstrate a 9.3% increase in precision and a 19% increase in recall compared to the baseline YOLOv1.1 model. This improvement validates the high potential of the proposed framework in detecting and localizing low-contrast and subtle lesions that might be overlooked by the baseline model.
Discussion: Integrating the Coordinate Attention mechanism into the YOLOv1.1 architecture significantly enhances the model's ability to understand long-range spatial dependencies and precisely localize subtle abnormalities. The proposed framework can serve as an efficient computer-aided diagnosis tool to mitigate human errors arising from missing small lesions in clinical settings.
Keywords: Chest Radiography, Subtle Pathologies, YOLOv1.1, Coordinate Attention Mechanism, Object Detection
Methods: In this study, the YOLO-v1.1 model was upgraded by strategically integrating the Coordinate Attention module into its backbone blocks to extract position-sensitive information and preserve the spatial details of small lesions. For training and evaluation, the standard VdNv-CXR dataset, comprising 15,000 radiographic images with annotations for 14 common types of abnormalities, was utilized. Pre-processing included normalization and the CLAHE technique to enhance the contrast of low-visibility findings.
Results: The research findings indicated that the proposed YOLO-CA-NET model achieved a mean Average Precision (mAP) of 0.329 and a mean Recall of 0.514. These results demonstrate a 9.3% increase in precision and a 19% increase in recall compared to the baseline YOLOv1.1 model. This improvement validates the high potential of the proposed framework in detecting and localizing low-contrast and subtle lesions that might be overlooked by the baseline model.
Discussion: Integrating the Coordinate Attention mechanism into the YOLOv1.1 architecture significantly enhances the model's ability to understand long-range spatial dependencies and precisely localize subtle abnormalities. The proposed framework can serve as an efficient computer-aided diagnosis tool to mitigate human errors arising from missing small lesions in clinical settings.
Keywords: Chest Radiography, Subtle Pathologies, YOLOv1.1, Coordinate Attention Mechanism, Object Detection
Keywords: Thoracic Radiography, Subtle Pathologies, YOLOv11, Coordinate Attention Mechanism, Object Detection.
Type of Study: Research |
Subject:
Special
Received: 2025/06/3 | Accepted: 2025/09/21 | Published: 2025/06/22
Received: 2025/06/3 | Accepted: 2025/09/21 | Published: 2025/06/22
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