Pneumonia Detection on chest X-ray images Using Ensemble of Deep
  Convolutional Neural Networks
Alhassan Mabrouk Rebeca P. Díaz Redondo Abdelghani Dahou Mohamed Abd Elaziz Mohammed Kayed
Pneumonia is a life-threatening lung infection resulting from several
different viral infections. Identifying and treating pneumonia on chest X-ray
images can be difficult due to its similarity to other pulmonary diseases.
Thus, the existing methods for predicting pneumonia cannot attain substantial
levels of accuracy. Therefore, this paper presents a computer-aided
classification of pneumonia, coined as Ensemble Learning (EL), to simplify the
diagnosis process on chest X-ray images. Our proposal is based on Convolutional
Neural Network (CNN) models, which are pre-trained CNN models that have been
recently employed to enhance the performance of many medical tasks instead of
training CNN models from scratch. We propose to use three well-known CNN
pre-trained (DenseNet169, MobileNetV2 and Vision Transformer) using the
ImageNet database. Then, these models are trained on the chest X-ray data set
using fine-tuning. Finally, the results are obtained by combining the extracted
features from these three models during the experimental phase. The proposed EL
approach outperforms other existing state-of-the-art methods, and it obtains an
accuracy of 93.91% and a F1-Score of 93.88% on the testing phase.