Abstract: Pulmonary disorders such as pneumonia remain a major clinical challenge, increasing the need for rapid and dependable diagnostic methods. This study proposes a machine learning–based system that examines both patient-reported symptoms and chest X-ray scans to estimate the probability of pneumonia. An initial risk score is generated using a weighted survey analysis, after which the chest radio-graphs are processed using MobileNetV2 for feature extraction and fed into a Convolutional Neural Network (CNN) for classification. By combining symptom evaluation with automated image interpretation, the system improves diagnostic accuracy and reduces reliance on manual assessment. This integrated approach supports faster screening and enhances the overall efficiency of pulmonary disease detection.
Keywords: Machine Learning, Deep Learning, CNN, MobileNetV2, Chest X-ray.
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DOI:
10.17148/IJARCCE.2025.141260
[1] Mrs. Nethravathi K.G, Kavya S, Gagana Shree S, Keerthana B, Ganashree C. N, "PULMONARY DISEASE PREDICTION USING MACHINE LEARNING," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2025.141260
