ChxCapsNet: Deep capsule network with transfer learning for evaluating pneumonia in paediatric chest radiographs

Abstract

Pneumonia is the primary cause of death in children under the age of 5 years. Faster and more accurate laboratory testing aids in the prescription of appropriate treatment for children suspected of having pneumonia, lowering mortality. In this work, we implement a deep neural network model to efficiently evaluate pediatric pneumonia from chest radio graph images. Our network uses a combination of convolutional and capsule layers to capture abstract details as well as low level hidden features from the radio graphic images, allowing the model to generate more generic predictions. Furthermore, we employ transfer learning approach to extract spatial features from the raw input radio graph images, allowing the model to save resources while enhancing performance. The capsule layer weights of the network are updated using the dynamic routing algorithm. The proposed model is evaluated using benchmark pneumonia dataset Kermany et al. 2018, and the outcomes of our experimental studies indicate that the capsules employed in the network enhance the learning of disease level features that are essential in diagnosing pneumonia. According to our comparison studies, the proposed model with Convolution base from InceptionV3 attached with Capsule layers at the end surpasses several existing models by achieving an accuracy of 94.84%. The proposed model is superior in terms of various performance measures such as accuracy and recall, and is well suited to real-time pediatric pneumonia diagnosis, substituting manual chest radiography examination.