Auto encoder-guided Feature Extraction for Pneumonia Identification from Chest X-ray Images

GNA University, Phagwara, Punjab, India

^* Corresponding author: neeta.rana@gnauniversity.edu.in

Abstract

The World Health Organization recognizes pneumonia as a significant global health issue. Artificial intelligence, particularly machine learning, and deep learning has emerged as valuable tools for improving pneumonia diagnosis. However, these techniques face a major challenge: the lack of labeled data. To tackle this, we propose using unsupervised learning models, which can produce comparable results even with limited training data. Our study presents an unsupervised learning approach utilizing autoencoders to detect pneumonia from chest X-ray images. Our method uses Variational autoencoders for feature extraction, which are then employed in classification using a Random Forest classifier. The model is trained on a dataset containing two classes of X-ray images: pneumonia and normal. Our approach demonstrates effectiveness comparable to existing supervised learning methods.