Enhancing Paediatric Pneumonia Detection and Classification Using Customized CNNs and Transfer Learning Based Ensemble Models

Abstract

Pneumonia is one of the most prominent causes of mortality in children who are below the age of five years in most parts of the globe. Hence, adequate pneumonia diagnosis is of paramount importance and is what drove this research effort which has led to the development of two transfer learning-based ensemble models. One of the proposed models classifies the chest radiographs into normal and pneumonia cases with outputs being generated from VGG-16, Inception-v3, and two custom-made convolutional neural networks, PneumoNet-v1 and PneumoNet-v2. The second model distinguishes bacterial from viral pneumonia with the help of Xception, MobileNet-v2, and PneumoNet-v1. To accomplish the aim of the study, the Guangzhou Women and Children’s Medical Center dataset (Kermany Dataset) was used to benchmark model performance. PneumoNet-v1 and PneumoNet-v2 were designed with an emphasis for high classification accuracy and have individual accuracies of 96.2% and 96.8%, respectively for pneumonia detection. The first ensemble model used for classifying between healthy and infected images attained a classification accuracy of 98.03%. The second model used for differentiating between bacterial and viral demonstrated an accuracy of 91.93%. The effectiveness of transfer learning-based ensemble models as well as of the proposed custom CNN designs in enhancing the analysis of paediatric pneumonia and facilitating better diagnosis has been explored in this research.