ArticleBMC medical imaging2023
Diagnosis and detection of pneumonia using weak-label based on X-ray images: a multi-center study.
Article in BMC medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for pneumonia diagnosis: a systematic review of multimodal learning, fusion strategies, and explainability.Frontiers in digital health · 2026Pooled it
- AI-driven multimodal imaging fusion using swin transformer and optimized tensor fusion networks for pneumonia detection.Scientific reports · 2026Article
- PneumoNet: Deep Neural Network for Advanced Pneumonia Detection.Current medical imaging · 2025Article
- Enhanced ResNet-50 with Multi-Feature Fusion for Robust Detection of Pneumonia in Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 2025Article
- Automated classification of chest X-rays: a deep learning approach with attention mechanisms.BMC medical imaging · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
purposeDevelopment and assessment the deep learning weakly supervised algorithm for the classification and detection pneumonia via X-ray.
methodsThis retrospective study analyzed two publicly available dataset that contain X-ray images of pneumonia cases and normal cases. The first dataset from Guangzhou Women and Children's Medical Center. It contains a total of 5,856 X-ray images, which are divided into training, validation, and test sets with 8:1:1 ratio for algorithm training and testing. The deep learning algorithm ResNet34 was employed to build diagnostic model. And the second public dataset were collated by researchers from Qatar University and the University of Dhaka along with collaborators from Pakistan and Malaysia and some medical doctors. A total of 1,300 images of COVID-19 positive cases, 1,300 normal images and 1,300 images of viral pneumonia for external validation. Class activation map (CAM) were used to location the pneumonia lesions.
resultsThe ResNet34 model for pneumonia detection achieved an AUC of 0.9949 [0.9910-0.9981] (with an accuracy of 98.29% a sensitivity of 99.29% and a specificity of 95.57%) in the test dataset. And for external validation dataset, the model obtained an AUC of 0.9835[0.9806-0.9864] (with an accuracy of 94.62%, a sensitivity of 92.35% and a specificity of 99.15%). Moreover, the CAM can accurately locate the pneumonia area.
conclusionThe deep learning algorithm can accurately detect pneumonia and locate the pneumonia area based on weak supervision information, which can provide potential value for helping radiologists to improve their accuracy of detection pneumonia patients through X-ray images.
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