Comparative Analysis of Swin Transformer and Residual Neural Network for Pneumonia Classification
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PPneumonia is a respiratory infectious disease that causes fluids to fill the lungs. It is considered one of the leading causes of infection-related deaths in children and seniors worldwide. Clinicians usually use chest X-ray images to diagnose pneumonia. However, pneumonia is prone to be misdiagnosed because it overlaps with cold and flu, causing severe and critical medical complications. Consequently, alternative supportive diagnostic methods are needed to minimize human errors and assist clinicians. Several attempts have used artificial intelligence systems, mainly in deep learning methods, to assist clinicians in early pneumonia diagnoses. However, further studies are required to consolidate the use of deep learning as an assistant tool to diagnose pneumonia accurately. In this study, we examine the Swin Transformer and the Residual Neural Network’s performance in classifying pneumonia and healthy chest X-ray images using the Guangzhou Women and Children’s Medical Center dataset and the COVID19, Pneumonia and Normal Chest X-ray Posteroanterior dataset. The experiment results demonstrate that the Swin Transformer achieves an accuracy of 98.9% in the Chest X-ray images dataset and 92.35% in the COVID19, Pneumonia and Normal Chest X-ray Posteroanterior dataset, while the Residual Neural Network achieves an accuracy of 97.9% and 88.8% respectively in classifying pneumonia. These results indicate that the Swin Transformer outperforms the Residual Neural Network as a tool for assisting clinicians in diagnosing pneumonia. Thus, the Swin Transformer may help in early decision-making, leading to treatment initiation and improving patient's health.
Abstract
PPneumonia is a respiratory infectious disease that causes fluids to fill the lungs. It is considered one of the leading causes of infection-related deaths in children and seniors worldwide. Clinicians usually use chest X-ray images to diagnose pneumonia. However, pneumonia is prone to be misdiagnosed because it overlaps with cold and flu, causing severe and critical medical complications. Consequently, alternative supportive diagnostic methods are needed to minimize human errors and assist clinicians. Several attempts have used artificial intelligence systems, mainly in deep learning methods, to assist clinicians in early pneumonia diagnoses. However, further studies are required to consolidate the use of deep learning as an assistant tool to diagnose pneumonia accurately. In this study, we examine the Swin Transformer and the Residual Neural Network’s performance in classifying pneumonia and healthy chest X-ray images using the Guangzhou Women and Children’s Medical Center dataset and the COVID19, Pneumonia and Normal Chest X-ray Posteroanterior dataset. The experiment results demonstrate that the Swin Transformer achieves an accuracy of 98.9% in the Chest X-ray images dataset and 92.35% in the COVID19, Pneumonia and Normal Chest X-ray Posteroanterior dataset, while the Residual Neural Network achieves an accuracy of 97.9% and 88.8% respectively in classifying pneumonia. These results indicate that the Swin Transformer outperforms the Residual Neural Network as a tool for assisting clinicians in diagnosing pneumonia. Thus, the Swin Transformer may help in early decision-making, leading to treatment initiation and improving patient's health.
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