Artificial Intelligence for Early Detection of Chest Nodules in X-ray Images

Hwa Yen Chiu, Rita Huan Ting Peng, Yi Chian Lin, Ting Wei Wang, Ya Xuan Yang, Ying Ying Chen, Mei Han Wu, Tsu Hui Shiao, Heng Sheng Chao, Yuh Min Chen, Yu Te Wu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Early detection increases overall survival among patients with lung cancer. This study formulated a machine learning method that processes chest X-rays (CXRs) to detect lung cancer early. After we preprocessed our dataset using monochrome and brightness correction, we used different kinds of preprocessing methods to enhance image contrast and then used U-net to perform lung segmentation. We used 559 CXRs with a single lung nodule labeled by experts to train a You Only Look Once version 4 (YOLOv4) deep-learning architecture to detect lung nodules. In a testing dataset of 100 CXRs from patients at Taipei Veterans General Hospital and 154 CXRs from the Japanese Society of Radiological Technology dataset, the sensitivity of the AI model using a combination of different preprocessing methods performed the best at 79%, with 3.04 false positives per image. We then tested the AI by using 383 sets of CXRs obtained in the past 5 years prior to lung cancer diagnoses. The median time from detection to diagnosis for radiologists assisted with AI was 46 (3–523) days, longer than that for radiologists (8 (0–263) days). The AI model can assist radiologists in the early detection of lung nodules.

Original languageEnglish
Article number2839
JournalBiomedicines
Volume10
Issue number11
DOIs
StatePublished - Nov 2022

Keywords

  • AI
  • artificial intelligence
  • detection
  • lung cancer
  • machine learning

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