Comparison of EfficientNet CNN models for multi-label chest X-ray disease diagnosis.

ABSTRACT: The analysis of chest X-ray images, which are critical for the early diagnosis of many diseases, is a difficult and time-consuming process due to the multiple labeling requirements and similar looking pathologies. In traditional methods, expert physicians analyze high-resolution chest X-ray images to diagnose these diseases using observational methods, a process that can lead to human error and hence misdiagnosis or underdiagnosis. In this study, we aim to autonomously detect 14 different diseases that significantly affect human health and some cases even lead to death using chest X-ray images in a multi-class manner using deep learning techniques. Previous studies on chest X-ray images focus on a single disease or have low success rates, and the architectures presented in previous studies

SUBMITTER: Ucan M 

PROVIDER: S-EPMC12453788 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature