Early identification of abnormal pulmonary infectious diseases using unsupervised anomaly detection.

ABSTRACT:

Background

The early identification of abnormal pulmonary infectious diseases (APIDs) could effectively control the large-scale spread of such diseases. This study proposed a deep learning-based method for the early identification of APIDs.

Methods

Unsupervised anomaly detection (UAD) refers to the identification of abnormal samples of which its distribution differs from that of normal samples using a training set comprised of only normal samples. Building on this principle, we proposed a method for the early identification of APIDs. First, we established a pulmonary infection computed tomography (PICT) image sequence dataset, which included computed tomography (CT) image sequences of various common pulmonary infections, as well as two known abnormal cases [coronavirus disea

SUBMITTER: Liu R 

PROVIDER: S-EPMC12682555 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature