Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks

• Main Authors: Dahou, A. (Author), Elaziz, M.A (Author), Kayed, M. (Author), Mabrouk, A. (Author), Redondo, R.P.D (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: deep learning; ensemble deep learning; image processing; medical image classification; vision transformer
• Online Access: View Fulltext in Publisher

 Pneumonia is a life-threatening lung infection resulting from several different viral infec-tions. 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. This paper presents a computer-aided classification of pneumonia, coined Ensemble Learning (EL), to simplify the diagnosis process on chest X-ray images. Our pro-posal is based on Convolutional Neural Network (CNN) models, which are pretrained 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 CNNs (DenseNet169, MobileNetV2, and Vision Transformer) pretrained using the ImageNet database. 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 obtains an accuracy of 93.91% and a F1-score of 93.88% on the testing phase. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.