In this work, we investigate the performance across multiple classification models to classify chest X-ray images into four categories of COVID-19, pneumonia, tuberculosis (TB), and normal cases. We leveraged transfer learning techniques with state-of-the-art pre-trained Convolutional Neural Networks (CNNs) models. We fine-tuned these pre-trained architectures on a labeled medical x-ray images. The initial results are promising with high accuracy and strong performance in key classification metrics such as precision, recall, and F1 score. We applied Gradient-weighted Class Activation Mapping (Grad-CAM) for model interpretability to provide visual explanations for classification decisions, improving trust and transparency in clinical applications.
@article{arxiv.2505.22609,
title = {Chest Disease Detection In X-Ray Images Using Deep Learning Classification Method},
author = {Alanna Hazlett and Naomi Ohashi and Timothy Rodriguez and Sodiq Adewole},
journal= {arXiv preprint arXiv:2505.22609},
year = {2025}
}