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

• URL: http://arxiv.org/abs/2312.07965v1
• Date: Wed, 13 Dec 2023 08:28:21 GMT
• Title: Pneumonia Detection on chest X-ray images Using Ensemble of Deep Convolutional Neural Networks
• Authors: Alhassan Mabrouk, Rebeca P. D\'iaz Redondo, Abdelghani Dahou, Mohamed Abd Elaziz, Mohammed Kayed
• Abstract summary: 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. 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.
• Score: 7.232767871756102
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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