New patch-based strategy for COVID-19 automatic identification using chest x-ray images.

ABSTRACT:

Purpose

The development of a robust model for automatic identification of COVID-19 based on chest x-rays has been a widely addressed topic over the last couple of years; however, the scarcity of good quality images sets, and their limited size, have proven to be an important obstacle to obtain reliable models. In fact, models proposed so far have suffered from over-fitting erroneous features instead of learning lung features, a phenomenon known as shortcut learning. In this research, a new image classification methodology is proposed that attempts to mitigate this problem.

Methods

To this end, annotation by expert radiologists of a set of images was performed. The lung region was then segmented and a new classification strategy based on a patch partitioning that improves the

SUBMITTER: Portal-Diaz JA 

PROVIDER: S-EPMC9647770 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature