article · Scientific Reports
Pneumonia remains a leading cause of infant mortality globally, yet interpreting chest X-rays is complex and frequently results in diagnostic disagreement among experienced radiologists. Early diagnosis is essential to mitigate the impact of the disease, making computer-aided diagnostics a valuable support tool. To improve classification accuracy, a Quaternion Channel-Spatial Attention network was developed by integrating spatial and channel attention mechanisms into a quaternion residual network. Attention mechanisms emulate human visual focus by concentrating on informative sections of an image while disregarding irrelevant areas. When evaluated on a Kaggle chest X-ray dataset, the network achieved an accuracy of 94.53 per cent and an area under the curve of 0.89. The results confirm that incorporating attention mechanisms into quaternion neural networks improves overall classification performance.
Pneumonia is the primary cause of infant mortality worldwide, making prompt and dependable detection critical. Because reading chest radiographs is intricate and subject to conflicting interpretations among radiologists, automated diagnostic tools can improve diagnostic consistency and enable earlier intervention to protect patient health.
This technology is an early-stage diagnostic algorithm tested on a benchmark Kaggle dataset. It could potentially serve as a computer-aided diagnostic tool for healthcare providers and radiologists assessing respiratory conditions. Real-world deployment would require substantial clinical validation, but the system could eventually integrate into medical imaging software to assist clinicians with image evaluation.
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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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DOI: 10.1038/s41598-023-35922-x
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