Pneumonia Identification Using Explainable Artificial Intelligence

Year : 2025 | Volume : 12 | Issue : 03 | Page : 01 11
    By

    Arul Antran Vijay S.,

  • Alagu Aravind A.,

  • Arvind R.,

  • Prasath B.,

  • Sanjay S.,

  1. Associate Professor, Department of Computer Science, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
  2. Student, Department of Computer Science, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
  3. Student, Department of Computer Science, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
  4. Student, Department of Computer Science, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
  5. Student, Department of Computer Science, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India

Abstract

Pneumonia, including tuberculosis (TB), remains one of the leading causes of death worldwide, especially in regions where access to healthcare is limited. Early and accurate diagnosis is critical for effective treatment and better patient outcomes, but traditional methods are time-consuming and require specialized expertise. This study explores the use of advanced deep learning models VGG16, VGG19, and ResNet50 to detect pneumonia and TB from chest X-ray images. By leveraging transfer learning, these models utilize pre-trained neural networks to extract meaningful features, while Grad-CAM visualizations provide insights into the decision-making process, ensuring transparency and trust in AI predictions. This research demonstrates the potential of combining cutting-edge deep learning with explainable AI to overcome diagnostic challenges in resource-limited settings. By focusing on interpretability and efficiency, the proposed approach aims to empower healthcare providers with reliable tools for timely and accurate diagnoses. Future efforts will focus on testing these models on larger and more diverse datasets, bringing us closer to integrating AI into everyday clinical workflows.

Keywords: Pneumonia, tuberculosis, deep learning, ResNet50, VGG16, VGG19, Grad-CAM, explainable AI

[This article belongs to Journal of Advanced Database Management & Systems ]

How to cite this article:
Arul Antran Vijay S., Alagu Aravind A., Arvind R., Prasath B., Sanjay S.. Pneumonia Identification Using Explainable Artificial Intelligence. Journal of Advanced Database Management & Systems. 2025; 12(03):01-11.
How to cite this URL:
Arul Antran Vijay S., Alagu Aravind A., Arvind R., Prasath B., Sanjay S.. Pneumonia Identification Using Explainable Artificial Intelligence. Journal of Advanced Database Management & Systems. 2025; 12(03):01-11. Available from: https://journals.stmjournals.com/joadms/article=2025/view=229337


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Regular Issue Subscription Review Article
Volume 12
Issue 03
Received 06/03/2025
Accepted 05/09/2025
Published 15/10/2025
Publication Time 223 Days
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