Novel large empirical study of deep transfer learning for COVID-19 classification based on CT and X-ray images.

ABSTRACT: The early and highly accurate prediction of COVID-19 based on medical images can speed up the diagnostic process and thereby mitigate disease spread; therefore, developing AI-based models is an inevitable endeavor. The presented work, to our knowledge, is the first to expand the model space and identify a better performing model among 10,000 constructed deep transfer learning (DTL) models as follows. First, we downloaded and processed 4481 CT and X-ray images pertaining to COVID-19 and non-COVID-19 patients, obtained from the Kaggle repository. Second, we provide processed images as inputs to four pre-trained deep learning models (ConvNeXt, EfficientNetV2, DenseNet121, and ResNet34) on more than a million images from the ImageNet database, in which we froze the convolutional and pooling la

SUBMITTER: Almutaani M 

PROVIDER: S-EPMC11532342 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature