Pneumonia and COVID-19 Classification Using VGG16 Architecture

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@article{1704268,
      author = {Kuber Anand, Ishaan Taneja, Bhaskar Kapoor, Sunil Maggu},
      title = {Pneumonia and COVID-19 Classification Using VGG16 Architecture},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {10},
      pages = {508-514},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704268.pdf},
      abstract = {Pneumonia, a disease in the lungs, has been found to be one of the most pernicious of diseases and has been a major cause of death among children. This disease is predominantly caused by viruses, bacteria or fungi. COVID-19, however, a more novel disease and a form of pneumonia, has hit the world by storm by claiming a hefty amount of lives in the past few years. The primary focus of this paper is to provide aid to the medical infrastructure by providing an efficient and accurate Machine Learning model having its foundations upon Convolutional Neural Networks (CNN) which can help patients differentiate whether they have Pneumonia or COVID-19,  based upon scanned copies of their chest X-Rays. Training of the model was done with the help of a dataset containing Chest X-Ray images available on Kaggle. We put forward this model with the help of VGG16,a form of a CNN model which implements 16 convolutional layers to achieve its results. The results obtained by the model signifies that Deep Learning can be used in detecting COVID-19 and Pneumonia, hence providing help to the medical system.},
      month = {April},
  }