An improvement of the CNN-XGboost model for pneumonia disease classification

X-ray images are viewed as a vital component in emergency diagnosis. They are often used by deep learning applications for disease prediction, especially for thoracic pathologies. Pneumonia, a fatal thoracic disease induced by bacteria or viruses, generates a pleural effusion where fluids are accumulated inside lungs, leading to breathing difficulty. The utilization of X-ray imaging for pneumonia detection offers several advantages over other modalities such as computed tomography scans or magnetic resonance imaging. X-rays provide a cost-effective and easily accessible method for screening and diagnosing pneumonia, allowing for quicker assessment and timely intervention. However, interpretation of chest X-ray images depends on the radiologist's competency. Within this study, we aim to suggest new elements leading to good interpretation of chest X-ray images for pneumonia detection, especially for distinguishing between viral and bacterial pneumonia.

We proposed an interpretation model based on convolutional neural networks (CNNs) and extreme gradient boosting (XGboost) for pneumonia classification. The experimental study is processed through various scenarios, using Python as a programming language and a public database obtained from Guangzhou Women and Children's Medical Centre.

The results demonstrate an acceptable accuracy of 87% within a mere 7 seconds, thereby endorsing its effectiveness compared to similar existing works.

Our study provides a model based on CNN and XGboost to classify images of viral and bacterial pneumonia. The work is a challenging task due to the lack of appropriate data. The experimental process allows a better accuracy of 87%, a specificity of 89%, and a sensitivity of 85%.