Diagnosis of Patients with Viral, Bacterial, and Non-Pneumonia Based on Chest X-Ray Images Using Convolutional Neural Networks
This work addresses pneumonia diagnosis, a critical global health issue, but is incremental as it applies existing methods like transfer learning and SVM to a specific medical dataset.
The paper tackled the problem of classifying patients into viral, bacterial, and non-pneumonia categories using chest X-ray images, achieving accuracies of 91.02% for pneumonia vs. non-pneumonia and 93.66% for viral vs. bacterial pneumonia.
According to the World Health Organization (WHO), pneumonia is a disease that causes a significant number of deaths each year. In response to this issue, the development of a decision support system for the classification of patients into those without pneumonia and those with viral or bacterial pneumonia is proposed. This is achieved by implementing transfer learning (TL) using pre-trained convolutional neural network (CNN) models on chest x-ray (CXR) images. The system is further enhanced by integrating Relief and Chi-square methods as dimensionality reduction techniques, along with support vector machines (SVM) for classification. The performance of a series of experiments was evaluated to build a model capable of distinguishing between patients without pneumonia and those with viral or bacterial pneumonia. The obtained results include an accuracy of 91.02%, precision of 97.73%, recall of 98.03%, and an F1 Score of 97.88% for discriminating between patients without pneumonia and those with pneumonia. In addition, accuracy of 93.66%, precision of 94.26%, recall of 92.66%, and an F1 Score of 93.45% were achieved for discriminating between patients with viral pneumonia and those with bacterial pneumonia.