Abstract:

Pneumonia, a severe lung infection, continues to be a leading cause of morbidity and mortality globally, particularly in vulnerable populations. Early and accurate detection of pneumonia is crucial for timely treatment and better patient outcomes. In this study, we propose an ensemble-based approach for pneumonia detection using chest X-ray images.

The proposed system integrates multiple deep learning models, including Convolutional Neural Networks (CNNs) and other advanced architectures, to enhance the precision and robustness of the detection process. Each model is independently trained on a labeled dataset, and the ensemble strategy combines their predictive strengths to reduce false positives and negatives.

The ensemble technique leverages model diversity to increase detection accuracy, particularly in complex cases where pneumonia symptoms may be subtle. The system’s performance is evaluated using metrics such as accuracy, precision, recall, and F1-score, demonstrating its potential to outperform single model-based approaches.

The proposed ensemble method offers a promising solution for clinical adoption, aiding healthcare professionals in more reliable pneumonia diagnosis and supporting the development of automated diagnostic tools.

Keywords:
Dataset of pneumonia ,
CNN algorithm

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