BOOSTED LIGHTWEIGHT ENSEMBLE LEARNING FOR MULTI-CLASS CHEST X-RAY CLASSIFICATION

Rapid and reliable identification of respiratory diseases from chest X-ray images remains an important task in computer-aided medical diagnosis. This study proposes a lightweight, ensemble-based boosting framework for multi-class classification of chest X-ray images into COVID-19, pneumonia, and normal categories. The proposed method integrates three efficient convolutional neural network architectures, namely DenseNet121, MobileNetV2, and NASNetMobile, and combines their outputs through a boosted ensemble mechanism to improve predictive performance while preserving computational efficiency. The dataset was split into training, validation, and test sets, and the images were resized, normalised, and augmented prior to model training. Experimental results showed that the proposed boosted ensemble model achieved a classification accuracy of 97.40% , outperforming the individual base models DenseNet121 ( 91.60% ), MobileNetV2 ( 94.40% ), and NASNetMobile ( 93.46% ). These findings indicate that boosted lightweight ensemble learning can provide an effective and practical solution for automated chest X-ray image classification in medical imaging applications.

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