{
  "id": "2112.02478",
  "version": "v2",
  "published": "2021-12-05T05:04:38.000Z",
  "updated": "2022-05-26T05:43:32.000Z",
  "title": "Classification of COVID-19 on chest X-Ray images using Deep Learning model with Histogram Equalization and Lungs Segmentation",
  "authors": [
    "Hitendra Singh Bhadouria",
    "Krishan Kumar",
    "Aman Swaraj",
    "Karan Verma"
  ],
  "comment": "Total number of words of the manuscript- 6577 The number of words of the abstract- 238 The number of figures- 8 The number of tables- 10",
  "categories": [
    "eess.IV",
    "cs.CV",
    "cs.LG"
  ],
  "abstract": "Background and Objective: Artificial intelligence (AI) methods coupled with biomedical analysis has a critical role during pandemics as it helps to release the overwhelming pressure from healthcare systems and physicians. As the ongoing COVID-19 crisis worsens in countries having dense populations and inadequate testing kits like Brazil and India, radiological imaging can act as an important diagnostic tool to accurately classify covid-19 patients and prescribe the necessary treatment in due time. With this motivation, we present our study based on deep learning architecture for detecting covid-19 infected lungs using chest X-rays. Dataset: We collected a total of 2470 images for three different class labels, namely, healthy lungs, ordinary pneumonia, and covid-19 infected pneumonia, out of which 470 X-ray images belong to the covid-19 category. Methods: We first pre-process all the images using histogram equalization techniques and segment them using U-net architecture. VGG-16 network is then used for feature extraction from the pre-processed images which is further sampled by SMOTE oversampling technique to achieve a balanced dataset. Finally, the class-balanced features are classified using a support vector machine (SVM) classifier with 10-fold cross-validation and the accuracy is evaluated. Result and Conclusion: Our novel approach combining well-known pre-processing techniques, feature extraction methods, and dataset balancing method, lead us to an outstanding rate of recognition of 98% for COVID-19 images over a dataset of 2470 X-ray images. Our model is therefore fit to be utilized in healthcare facilities for screening purposes.",
  "revisions": [
    {
      "version": "v2",
      "updated": "2022-05-26T05:43:32.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "I.4.3",
      "I.4.6",
      "I.4.9"
    ],
    "keywords": [
      "chest x-ray images",
      "deep learning model",
      "histogram equalization",
      "lungs segmentation",
      "combining well-known pre-processing techniques"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}