International Journal on Science and Technology

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A.I. Based Pneumonia Diagnosis System

Author(s) Divyansh Singh Gehlot, Varad Waghmare, Prof. N.B. Pokale
Country India
Abstract Pneumonia continues to be a major cause of death globally, especially in areas with minimal radiological capability. In this paper, we describe an end-to-end AI system for pneumonia diagnosis using deep learning. We use a pre trained DenseNet-121 and fine-tune it on a filtered chest X-ray dataset available at Kaggle for binary pneumonia vs. normal classification. To facilitate accurate and transparent decision making, the framework incorporates Gradient-weighted Class Activation Mapping (Grad-CAM), which exhibits visually prominent clinically relevant areas of the input images. Thorough experimentation demonstrates an average test accuracy of about 89% with well-balanced performance among classes as evidenced by a thorough confusion matrix and
classification report. The system is also implemented as a web application on Gradio for real-time clinical decision support. Issues like data imbalance and interpretability of models are addressed in addition to proposed future enhancements and clinical integration.
Keywords —Pneumonia Detection, Medical Image Analysis, Artificial Intelligence in Healthcare, Deep Learning Models, Chest X-ray Classification, Convolutional Neural Network (CNN)
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-06-08
DOI https://doi.org/10.71097/IJSAT.v16.i2.6033
Short DOI https://doi.org/g9pz85
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