Automated processing of social media content for radiologists: applied deep learning to radiological content on twitter during COVID-19 pandemic.

ABSTRACT:

Purpose

The purpose of this study was to develop an automated process to analyze multimedia content on Twitter during the COVID-19 outbreak and classify content for radiological significance using deep learning (DL).

Materials and methods

Using Twitter search features, all tweets containing keywords from both "radiology" and "COVID-19" were collected for the period January 01, 2020 up to April 24, 2020. The resulting dataset comprised of 8354 tweets. Images were classified as (i) images with text (ii) radiological content (e.g., CT scan snapshots, X-ray images), and (iii) non-medical content like personal images or memes. We trained our deep learning model using Convolutional Neural Networks (CNN) on training dataset of 1040 labeled images drawn from all three classes. We th

SUBMITTER: Khurana S 

PROVIDER: S-EPMC7811945 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature