{
  "id": "2003.14363",
  "version": "v1",
  "published": "2020-03-31T16:48:27.000Z",
  "updated": "2020-03-31T16:48:27.000Z",
  "title": "Automated Methods for Detection and Classification Pneumonia based on X-Ray Images Using Deep Learning",
  "authors": [
    "Khalid El Asnaoui",
    "Youness Chawki",
    "Ali Idri"
  ],
  "categories": [
    "eess.IV",
    "cs.CV"
  ],
  "abstract": "Recently, researchers, specialists, and companies around the world are rolling out deep learning and image processing-based systems that can fastly process hundreds of X-Ray and computed tomography (CT) images to accelerate the diagnosis of pneumonia such as SARS, COVID-19, and aid in its containment. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as MERS, COVID-19. In this paper, we present a comparison of recent Deep Convolutional Neural Network (DCNN) architectures for automatic binary classification of pneumonia images based fined tuned versions of (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, MobileNet_V2 and Xception). The proposed work has been tested using chest X-Ray & CT dataset which contains 5856 images (4273 pneumonia and 1583 normal). As result we can conclude that fine-tuned version of Resnet50, MobileNet_V2 and Inception_Resnet_V2 show highly satisfactory performance with rate of increase in training and validation accuracy (more than 96% of accuracy). Unlike CNN, Xception, VGG16, VGG19, Inception_V3 and DenseNet201 display low performance (more than 84% accuracy).",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-03-31T16:48:27.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep learning",
      "classification pneumonia",
      "x-ray images",
      "automated methods",
      "densenet201 display low performance"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}