{
  "id": "2312.07965",
  "version": "v1",
  "published": "2023-12-13T08:28:21.000Z",
  "updated": "2023-12-13T08:28:21.000Z",
  "title": "Pneumonia Detection on chest X-ray images Using Ensemble of Deep Convolutional Neural Networks",
  "authors": [
    "Alhassan Mabrouk",
    "Rebeca P. Díaz Redondo",
    "Abdelghani Dahou",
    "Mohamed Abd Elaziz",
    "Mohammed Kayed"
  ],
  "comment": "14 pages, 4 figures, journal",
  "journal": "Applied Sciences, 2022, vol. 12, no 13, p. 6448",
  "doi": "10.3390/app12136448",
  "categories": [
    "eess.IV",
    "cs.CV",
    "cs.LG"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-12-13T08:28:21.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "chest x-ray images",
      "deep convolutional neural networks",
      "pneumonia detection",
      "cnn models",
      "chest x-ray data set"
    ],
    "tags": [
      "journal article"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 14,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}