Deep learning-based pneumonia detection from chest X-ray images using a convolutional neural network

Pneumonia remains a significant public health challenge, particularly in resource-limited settings where access to expert radiological diagnosis is scarce. This study proposes a deep learning-based approach using a custom Convolutional Neural Network (CNN) for the binary classification of chest X-ray images into “Pneumonia” and “Normal” categories. The model was trained and evaluated on a curated dataset of 5,856 chest X-ray images, incorporating data preprocessing and augmentation techniques to enhance generalizability. Evaluation of the proposed CNN yielded strong performance metrics, including an accuracy of 96.05%, a precision of 98.79%, a recall of 95.76%, and an AUC of 0.9921. The precision-recall curve also demonstrated an average precision score of 0.9970, confirming the model’s robustness, even under class imbalance. These results highlight the potential of the proposed CNN model to assist clinicians in rapid and accurate pneumonia diagnosis, supporting its applicability in clinical and low-resource healthcare environments.

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