Abstract
Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon at the right time and thus the early diagnosis of pneumonia is vital. The paper aims to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances in accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN): AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. A total of 5247 chest X-ray images consisting of bacterial, viral, and normal chest x-rays images were preprocessed and trained for the transfer learning-based classification task. In this study, the authors have reported three schemes of classifications: normal vs. pneumonia, bacterial vs. viral pneumonia, and normal, bacterial, and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial, and viral pneumonia were 98%, 95%, and 93.3%, respectively. This is the highest accuracy, in any scheme, of the accuracies reported in the literature. Therefore, the proposed study can be useful in more quickly diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.
Highlights
Pneumonia is considered the greatest cause of child fatalities all over the world.Approximately 1.4 million children die of pneumonia every year, which is 18% of the total children died at less than five years old [1]
It can be noted that, for three classification schemes, DenseNet201 is producing the highest accuracy for both training and testing
For normal 11 and pneumonia classification, the test accuracy was 98%, while, for normal, bacterial, and viral pneumonia classification, it was 93.3%, and, for bacterial and viral pneumonia classification, it was
Summary
Pneumonia is considered the greatest cause of child fatalities all over the world.Approximately 1.4 million children die of pneumonia every year, which is 18% of the total children died at less than five years old [1]. Pneumonia is a lung infection, which can be caused by either bacteria or viruses. This bacterial or viral infectious disease can be well treated by antibiotics and antivirals drugs. CNNs have been popular due to their improved performance in image. CNNs haveinbeen popular along due towith theirfilters improved in spatial image classification. The convolutional layers the network help inperformance extracting the classification. The convolutional layers in the network along with filters help in extracting the spatial and temporal features in an image. The layers have a weight-sharing technique, which helps in and temporal features in an[37,38]. The layers have a weight-sharing technique, which helps in reducing computation efforts reducing computation efforts are [37,38]
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