AI-Enhanced Pneumonia Detection with Visual Interpretability

Abrar Ahmed Shahok, Faizan Ali Memon, Kaleemullah Jalbani, M. Shoaib
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Abrar Ahmed Shahok, Faizan Ali Memon, Kaleemullah Jalbani, M. Shoaib: Department of Computer ScienceQuaid-e-Awam University of Engineering, Science and TechnologyNawabshah, Pakistan

International Journal of Innovations in Science & Technology, 2025, vol. 7, issue 6, 118-126

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.

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 (search for similar items in EconPapers)
Date: 2025
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