Comparing different deep learning architectures for classification of chest radiographs.

ABSTRACT: Chest radiographs are among the most frequently acquired images in radiology and are often the subject of computer vision research. However, most of the models used to classify chest radiographs are derived from openly available deep neural networks, trained on large image datasets. These datasets differ from chest radiographs in that they are mostly color images and have substantially more labels. Therefore, very deep convolutional neural networks (CNN) designed for ImageNet and often representing more complex relationships, might not be required for the comparably simpler task of classifying medical image data. Sixteen different architectures of CNN were compared regarding the classification performance on two openly available datasets, the CheXpert and COVID-19 Image Data Collection. Ar

SUBMITTER: Bressem KK 

PROVIDER: S-EPMC7423963 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature