AI-Enhanced Pneumonia Detection with Visual Interpretability

Authors

  • Abrar Ahmed Shahok Department of Computer Science Quaid-e-Awam University of Engineering, Science and Technology Nawabshah, Pakistan
  • Faizan Ali Memon Department of Computer Science Quaid-e-Awam University of Engineering, Science and Technology Nawabshah, Pakistan
  • Kaleemullah Jalbani Department of Computer Science Quaid-e-Awam University of Engineering, Science and Technology Nawabshah, Pakistan
  • M. Shoaib Department of Computer Science Quaid-e-Awam University of Engineering, Science and Technology Nawabshah, Pakistan

Keywords:

Deep Learning for Pneumonia Detection, Explainable AI in Medical Imaging, Pneumonia Classification with AI, Ethical AI in Healthcare, AI for Radiology and X-ray Analysis

Abstract

Pneumonia is a serious lung infection that can be life-threatening, particularly for young children, the elderly, and people with weakened immune systems. Early detection is crucial but difficult because pneumonia signs on X-rays can be subtle. Many AI tools can help diagnose pneumonia, but they often work like “black boxes,” making it hard for doctors to trust their decisions. This study introduces a mobile app that uses Convolutional Neural Networks (CNNs) to detect pneumonia from X-rays. To improve transparency, we use Explainable AI (XAI) to highlight the areas of the X-ray that influenced the diagnosis. Additionally, we integrate a Large Language Model (LLM) to generate clear, structured medical reports. Our goal is to create a trustworthy and user-friendly tool for doctors in real-world settings.

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Published

2025-05-17

How to Cite

Shahok, A. A., Memon, F. A., Jalbani, K., & M. Shoaib. (2025). AI-Enhanced Pneumonia Detection with Visual Interpretability. International Journal of Innovations in Science & Technology, 7(6), 118–126. Retrieved from https://journal.50sea.com/index.php/IJIST/article/view/1291