Pneumonia remains a critical public health issue, requiring accurate and efficient diagnostic support. This study presents a deep learning–based method for the automatic classification of chest X-ray images to assist clinicians in pneumonia detection. A convolutional neural network (CNN) was trained and validated on the NIH Chest X-ray dataset,incorporating tailored preprocessing and optimization techniques.
The proposed model achieved an accuracy exceeding 91% and an F1-score above 93%, demonstrating high robustness and outperforming conventional approaches. These results confirm the potential of deep learning to enhance diagnostic precision while reducing the annotation workload. Despite ongoing challenges—such as data quality, interpretability, and generalization—this work underlines the value of artificial intelligence as a promising decision-support tool in medical imaging.
Keywords— medical imaging, artificial intelligence, deep learning, thoracic imaging, pneumonia.
Auteurs : Bourkache Noureddine , Laghrouche Mourad, Sahbi Sidhom
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