A Lightweight Convolutional Neural Network Model for Child Pneumonia Classification

Abstract

Pneumonia is still a serious threat for children including newborns. Each year many children died of pneumonia. Physicians diagnose pneumonia through some process including reviewing chest X-rays of patients. While reviewing, a single diagnostic mistake may cause a serious threat and do significant harm to patients. In recent years, Computer-aided detection system (CAD) and medical image classification are progressively turning into another research territory. CAD can reduce the physician's effort and help to review chest X-rays fast and error-free. Currently, Researchers build various models to detect pneumonia from chest X-rays. However, there is still a lack of computationally efficient models to diagnose pediatric pneumonia. Further, some off-the-shelf or pre-trained models are not always suitable for mobile and embedded vision applications since these models are not lightweight. In our research, a lightweight convolutional neural network model was built from scratch using basic building blocks which able to learn lung texture features and detect pediatric pneumonia. Our proposed model performance was compared with some off-the-shelf models. The proposed model achieved the best AUC (99.0%), test accuracy (94.6 %), F1 (94.7 %), precision (93.2 %) and specificity (93.1%) scores. Moreover, Several data augmentation algorithms were employed to increase the model's classification ability.