Detection & Classification of Pneumonia in Chest X-Ray Images Using Deep Learning Techniques

Pneumonia is a respiratory infection caused by bacteria or viruses; it affects many individuals, especially in developing and underdeveloped nations, where high levels of pollution, unhygienic living conditions, and overcrowding are relatively common, together with inadequate medical infrastructure. Pneumonia causes pleural effusion, a condition in which fluids fill the lung, causing respiratory difficulty. Early diagnosis of pneumonia is crucial to ensure curative treatment and increase survival rates. Chest X-ray imaging is the most frequently used method for diagnosing pneumonia. However, the examination of chest X-rays is a challenging task and is prone to subjective variability. In this study, we developed a computer-aided diagnosis system for automatic pneumonia detection using chest X-ray images. We employed deep transfer learning to handle the scarcity of available data and designed a Convolutional Neural Network (CNN) model along with the four transfer learning methods: CovXNet, RNN and VGG16. Where, in the existing methods ResNet 50 is used that which did not got the proper accuracy and that tend to be improved. Hence the present method with other transfer learning methods are proposed. The proposed approach was evaluated on publicly available pneumonia X-ray dataset.

Keywords: Pneumonia, Chest X-ray images. Deep Learning, CNN, CovXNet, RNN, VGG16.