Design ensemble deep learning model for pneumonia disease classification

Abstract

With the recent spread of the SARS-CoV-2 virus, computer-aided diagnosis (CAD) has received more attention. The most important CAD application is to detect and classify pneumonia diseases using X-ray images, especially, in a critical period as pandemic of covid-19 that is kind of pneumonia. In this work, we aim to evaluate the performance of single and ensemble learning models for the pneumonia disease classification. The ensembles used are mainly based on fined-tuned versions of (InceptionResNet_V2, ResNet50 and MobileNet_V2). We collected a new dataset containing 6087 chest X-ray images in which we conduct comprehensive experiments. As a result, for a single model, we found out that InceptionResNet_V2 gives 93.52% of F1 score. In addition, ensemble of 3 models (ResNet50 with MobileNet_V2 with InceptionResNet_V2) shows more accurate than other ensembles constructed (94.84% of F1 score).

Keywords: Computer-aided diagnosis; Covid-19; Deep learning; Ensemble deep learning; Pneumonia disease; Pneumonia multiclass classification; X-ray images.

Fig. 1

Flow diagram of proposed methodology

Fig. 2

Accuracy and loss curve and confusion matrix of InceptionResNet_V2

Fig. 3

Accuracy and loss curve and confusion matrix of MobileNet_V2

Fig. 4

Accuracy and loss curve and confusion matrix of ResNet50

Fig. 5

Performance measure of single and ensemble models