002: Pneumonia Detection via Convolutional Neural Networks (CNNs) based on X-ray Images

Pneumonia is an infection of the lungs. Sever diseases such as COVID-19, SARS, and ARDS onset Pneumonia resulting in lung injury and death according to Xu et al. (2020). Chest X-ray films are one of the most widely used tools in detecting lung infection. Automatically diagnosing Pneumonia at early phase can significantly prevent the rapid spread of the respiratory diseases and ease the workload at laboratories, which is essential especially in COVID-19 pandemic these days. Machine learning applications coupled with imaging techniques can be very useful in auto-detection of infected patients. Convolutional neural networks (CNN), as one of the subfield of machine learning, have remarkable performance in end-to-end machine learning for images. It requires minimal feature engineering and achieves near human performance on various benchmark tasks. In this project, we built a pipeline to preprocess image data and explore various neural networks to predict the label of chest X-rays. The model performance was evaluated based on the classification accuracy, which is defined as the percentage of correctly predicted labels, healthy or Pneumonia. Additionally, to have a better understanding of how the networks made such classification decision, saliency maps were used to diagnose decision bias.

Machine learning

Convolutional neural networks 

Implementation and Analysis

Conference on Statistical Practice (CSP) 2023