3D CNN Model for the Diagnosis of COVID-19 by Classification of Chest CT Scans

@article{1702820,
      author = {T. Gnana Jyothi, R. Uma Dhathri, Sk. Dilshadbe, M. Sree Valli},
      title = {3D CNN Model for the Diagnosis of COVID-19 by Classification of Chest CT Scans},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {5},
      number = {1},
      pages = {92-96},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1702820.pdf},
      abstract = {This paper will discuss the steps that are needed to build a 3D convolutional neural network (CNN) to predict the presence of viral pneumonia in Computer Tomography (CT) scans. 2D CNNs are commonly used to process RGB images (which have 3 channels). A 3D CNN is simply the 3D equivalent of 2D CNN. It takes a 3D volume or a sequence of 2D frames (e.g., slices in a CT scan) as input. To implement this project, we use a subset of the MosMedData: Chest CT Scans with COVID-19 Related Findings, which consists of  CT scans of the lungs with COVID-19 related findings and without such findings. We will be using the associated radiological findings of the CT scans as labels to build a classifier to predict the presence of viral pneumonia. Hence, the task is a binary classification problem.},
      keywords = {Convolutional Neural Network (CNN), COVID-19, CT scan, Viral Pneumonia.},
      month = {July},
  }