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Pneumonia is a life-threatening lung infection resulting from severaldifferent viral infections. Identifying and treating pneumonia on chest X-rayimages can be difficult due to its similarity to other pulmonary diseases.Thus, the existing methods for predicting pneumonia cannot attain substantiallevels of accuracy. Therefore, this paper presents a computer-aidedclassification of pneumonia, coined as Ensemble Learning (EL), to simplify thediagnosis process on chest X-ray images. Our proposal is based on ConvolutionalNeural Network (CNN) models, which are pre-trained CNN models that have beenrecently employed to enhance the performance of many medical tasks instead oftraining CNN models from scratch. We propose to use three well-known CNNpre-trained (DenseNet169, MobileNetV2 and Vision Transformer) using theImageNet database. Then, these models are trained on the chest X-ray data setusing fine-tuning. Finally, the results are obtained by combining the extractedfeatures from these three models during the experimental phase. The proposed ELapproach outperforms other existing state-of-the-art methods, and it obtains anaccuracy of 93.91% and a F1-Score of 93.88% on the testing phase.

Pneumonia Detection on chest X-ray images Using Ensemble of Deep Convolutional Neural Networks