Deep Learning-Based Object Detection Strategies for Disease Detection and Localization in Chest X-Ray Images.

ABSTRACT:

Background and objectives

Chest X-ray (CXR) images are commonly used to diagnose respiratory and cardiovascular diseases. However, traditional manual interpretation is often subjective, time-consuming, and prone to errors, leading to inconsistent detection accuracy and poor generalization. In this paper, we present deep learning-based object detection methods for automatically identifying and annotating abnormal regions in CXR images.

Methods

We developed and tested our models using disease-labeled CXR images and location-bounding boxes from E-Da Hospital. Given the prevalence of normal images over diseased ones in clinical settings, we created various training datasets and approaches to assess how different proportions of background images impact model performance. To addre

SUBMITTER: Cheng YC 

PROVIDER: S-EPMC11640298 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature