Pneumonia detection on chest x-ray images using residual convolutional neural network

Abstract

Pneumonia is a chest disease that occurs as a result of inflammation of the lung tissue. Although pneumonia can occur at any age, it is quite dangerous in people under the age of two and over the age of sixty-five. According to World Health Organization data, approximately 7% of all deaths in the world are due to pneumonia. Early diagnosis and treatment of the disease is an important factor in reducing mortality rates due to the disease. In the study, an effective convolutional neural network (ESA) model was proposed for pneumonia detection from three-dimensional (3D) chest x-ray images. The proposed model is designed using transfer learning approach with pre-trained ResNet. The performance of the model has now been increased by skipping block connections and some layers in the deep learning architecture. An open access dataset consisting of 5617 X-Ray images, labeled pneumonia and normal, was used in the analysis. The performance of the proposed method is compared with three different models. The first model is a simple ESA model. The second model is an ESA model in which some blocks are removed from the proposed model. The third model represents a widely used pre-trained network known as ResNet-18. According to the analysis, the accuracy, specificity, sensitivity, precision and F-1 score values of the proposed method are 98.42%, respectively; 97.52%; 99.35%; It was obtained as 97.47% and 98.90%. When the results obtained from the analyzes are examined, it is revealed that the proposed method is successful in detecting pneumonia from chest X-ray images.