ArticleScientific reports2023
Pneumonia detection with QCSA network on chest X-ray.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 31 citations in OpenAlex.
- AI-driven multimodal imaging fusion using swin transformer and optimized tensor fusion networks for pneumonia detection.Scientific reports · 2026Article
- HybridCATNet: an explainable CNN-Transformer framework with early multi-channel fusion and uncertainty estimation for pneumonia detection from chest X-rays.Frontiers in digital health · 2026Article
- Evaluation of Machine Learning Methods Developed for Prediction and Diagnosis of Pneumonia: A Systematic Review.Health science reports · 2025Review
- PneumoNet: Deep Neural Network for Advanced Pneumonia Detection.Current medical imaging · 2025Article
- Breast Cancer Detection Using Infrared Thermography: A Survey of Texture Analysis and Machine Learning Approaches.Bioengineering (Basel, Switzerland) · 2025Review
- Secure pulmonary diagnosis using transformer-based approach to X-ray classification with KL divergence optimization.Frontiers in medicine · 2025Article
- Enhancing pediatric pneumonia diagnosis through masked autoencoders.Scientific reports · 2024Article
- Efficient pneumonia detection using Vision Transformers on chest X-rays.Scientific reports · 2024Article
- Pneumonia detection on chest X-rays from Xception-based transfer learning and logistic regression.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Prediction of stroke patients' bedroom-stay duration: machine-learning approach using wearable sensor data.Frontiers in bioengineering and biotechnology · 2023Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Worldwide, pneumonia is the leading cause of infant mortality. Experienced radiologists use chest X-rays to diagnose pneumonia and other respiratory diseases. The diagnostic procedure's complexity causes radiologists to disagree with the decision. Early diagnosis is the only feasible strategy for mitigating the disease's impact on the patent. Computer-aided diagnostics improve the accuracy of diagnosis. Recent studies established that Quaternion neural networks classify and predict better than real-valued neural networks, especially when dealing with multi-dimensional or multi-channel input. The attention mechanism has been derived from the human brain's visual and cognitive ability in which it focuses on some portion of the image and ignores the rest portion of the image. The attention mechanism maximizes the usage of the image's relevant aspects, hence boosting classification accuracy. In the current work, we propose a QCSA network (Quaternion Channel-Spatial Attention Network) by combining the spatial and channel attention mechanism with Quaternion residual network to classify chest X-Ray images for Pneumonia detection. We used a Kaggle X-ray dataset. The suggested architecture achieved 94.53% accuracy and 0.89 AUC. We have also shown that performance improves by integrating the attention mechanism in QCNN. Our results indicate that our approach to detecting pneumonia is promising.
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