Determining Pneumonia via X-ray Image Using Neural Network

Abstract

Despite the fact that pneumonia is a vaccine-preventable disease, it has become a global health concern. Every year, 800,000 children die from pneumonia. Pneumonia has been the cause of death in 17 percent of Bangladeshi kids under the age of 5. X-rays of the chest can be used to detect it. A qualified radiologists is essential for analysis. Even with an experienced radiologist, analyzing chest X-rays is tough. Machines can readily complete jobs that are difficult for humans to complete. Pneumonia can be diagnosed using machine learning and image processing. Machine learning (ML) is a branch of artificial intelligence that helps computers learn and make decisions on their own. Efficient pretrained transfer learning based VGG16, VGG19 and ResNet50 architectures are used to develop the proposed model which could assist both doctors and patients. The convolutional neural network is one of the machine learning algorithms. Image processing has also been used to help computers comprehend photos and identify Pneumonia more precisely. We used augmentation approaches even after training our model with over 6,000 pictures. With the use of a chest x-ray report, users will be able to quickly determine if they have Pneumonia in a relatively short period of time. In order to improve the accuracy, we added several extra layers to all the model. To enhance the number of images in a balanced way, data augmentation techniques were applied. The comparison between VGG16, VGG19, and ResNet50 is shown in this project. Whereas, VGG16 has obtained 94.5 percent accuracy, which is the greatest among the other algorithms implemented, indicating that it can classify normal and pneumonia. Other image-based classification problems can also apply the proposed architecture.