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Automated Detection of Pneumonia Cases Using Deep Neural Networks with X-ray Images

Approximately two million pediatric deaths occur every year due to Pneumonia. Detection and diagnosis of Pneumonia plays an important role in reducing these deaths. Chest radiography is one of the most commonly used modalities to detect pneumonia. In this paper, we propose a deep learning architecture to detect pneumonia in chest radiographs. This architecture contains one network to classify images as either normal or pneumonic. In this paper, we adopt ResNet-50 architecture in this blog as it has proven to be highly effective for various medical imaging applications. Those X-ray images database contain 198 images. The data divided into 99 samples for the normal X-ray images and 99 samples for pneumonia X-ray images. The experiment results evaluated by three parameters including accuracy, sensitivity, and specificity. For the ResNet-50 deep learning, these outcomes refer to the maximum accuracy being 96.5% by the training dataset for the ResNet-50. ResNet-50 can be considered as a high sensitivity model to characterize and diagnose Pneumonia infections, and can be used as an adjuvant tool in radiology departments.