Abstract: Pneumonia is a potentially life-threatening respiratory infection that requires timely and accurate diagnosis for effective treatment. Traditional diagnostic methods, such as physical examination and radiologist interpretation of chest X-rays, are often time-consuming and susceptible to human error. This research presents an AI/ML-driven approach for automated pneumonia detection from chest X-ray images using advanced computer vision techniques. Leveraging convolutional neural networks (CNNs) and deep learning architectures, the system is trained and validated on a publicly available chest X-ray dataset. The model demonstrates high accuracy, sensitivity, and specificity in classifying pneumonia cases, thereby offering a reliable diagnostic aid. The integration of artificial intelligence in medical imaging not only accelerates the diagnostic process but also supports clinical decision-making, particularly in resource-constrained settings. This study highlights the potential of AI-powered tools in enhancing diagnostic efficiency and contributing to the broader goal of intelligent healthcare systems.
Keywords: Pneumonia Detection, Convolutional Neural Networks (CNN), Deep Learning, Medical Imaging, Image Classification, DICOM Processing, Healthcare Diagnostics, Lung Infection Detection, Computer Vision, Django Framework, Radiology Support System, Bounding Box Localization, AI-Driven Medical Analysis.
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DOI:
10.17148/IJARCCE.2025.14637
