• Corpus ID: 227228613

Uncertainty-driven ensembles of deep architectures for multiclass classification. Application to COVID-19 diagnosis in chest X-ray images

  title={Uncertainty-driven ensembles of deep architectures for multiclass classification. Application to COVID-19 diagnosis in chest X-ray images},
  author={Juan Eloy Arco and Andr{\'e}s Ortiz and Javier Ram{\'i}rez and Francisco J. Mart{\'i}nez-Murcia and Yu-Dong Zhang and Juan Manuel G{\'o}rriz},
Respiratory diseases kill million of people each year. Diagnosis of these pathologies is a manual, time-consuming process that has inter and intra-observer variability, delaying diagnosis and treatment. The recent COVID-19 pandemic has demonstrated the need of developing systems to automatize the diagnosis of pneumonia, whilst Convolutional Neural Network (CNNs) have proved to be an excellent option for the automatic classification of medical images. However, given the need of providing a… 
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