# Artificial Intelligence-Driven Prognostic Classification of COVID-19 Using Chest X-rays: A Deep Learning Approach

arXiv: https://arxiv.org/abs/2503.13277 · HTML: https://mlsanity.com/paper/2503.13277/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 51 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Can AI help in screening Viral and COVID-19 pneumonia?](https://mlsanity.com/paper/2003.13145/) (2020)
- [Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans](https://mlsanity.com/paper/2008.06388/) (2020)
- [Detection and severity classification of COVID-19 in CT images using deep learning](https://mlsanity.com/paper/2102.07726/) (2021)
- [Assessing Automated Machine Learning service to detect COVID-19 from X-Ray and CT images: A Real-time Smartphone Application case study](https://mlsanity.com/paper/2010.02715/) (2020)

## Abstract (arXiv)

Background: The COVID-19 pandemic has overwhelmed healthcare systems, emphasizing the need for AI-driven tools to assist in rapid and accurate patient prognosis. Chest X-ray imaging is a widely available diagnostic tool, but existing methods for prognosis classification lack scalability and efficiency. Objective: This study presents a high-accuracy deep learning model for classifying COVID-19 severity (Mild, Moderate, and Severe) using Chest X-ray images, developed on Microsoft Azure Custom Vision. Methods: Using a dataset of 1,103 confirmed COVID-19 X-ray images from AIforCOVID, we trained and validated a deep learning model leveraging Convolutional Neural Networks (CNNs). The model was evaluated on an unseen dataset to measure accuracy, precision, and recall. Results: Our model achieved an average accuracy of 97%, with specificity of 99%, sensitivity of 87%, and an F1-score of 93.11%. When classifying COVID-19 severity, the model achieved accuracies of 89.03% (Mild), 95.77% (Moderate), and 81.16% (Severe). These results demonstrate the model's potential for real-world clinical applications, aiding in faster decision-making and improved resource allocation. Conclusion: AI-driven prognosis classification using deep learning can significantly enhance COVID-19 patient management, enabling early intervention and efficient triaging. Our study provides a scalable, high-accuracy AI framework for integrating deep learning into routine clinical workflows. Future work should focus on expanding datasets, external validation, and regulatory compliance to facilitate clinical adoption.