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| Format: | Recurso digital |
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Zenodo
2022
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| Online Access: | https://doi.org/10.5281/zenodo.18398331 |
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Table of Contents:
- Pneumonia is a severe respiratory disease, affecting many across the world. In developing countries and areas without sufficient resources, pneumonia is especially prevalent, with fatal outcomes if not treated appropriately. Current testing methods pose many issues including cost, accessibility, and accuracy. Several research studies have shown the drastic improvements that early diagnosis of pneumonia can have, preventing fatal outcomes. Deep learning networks have been used to aid this problem, achieving state-of-the-art accuracies in swift diagnosis. However, the large computational costs associated with these standard algorithms prove them ineffective in developing and remote areas of the world. A shallow convolutional neural network was developed to detect pneumonia based on Chest X-Ray images. This network achieved a peak accuracy of 96.21%, demonstrating its practicality and significance in such areas.