A machine learning texture model for classifying lung cancer subtypes using preliminary bronchoscopic findings

Po Hao Feng, Yin Tzu Lin, Chung Ming Lo

研究成果: 雜誌貢獻文章

1 引文 斯高帕斯(Scopus)

摘要

Purpose: Bronchoscopy is useful in lung cancer detection, but cannot be used to differentiate cancer types. A computer-aided diagnosis (CAD) system was proposed to distinguish malignant cancer types to achieve objective diagnoses. Methods: Bronchoscopic images of 12 adenocarcinoma and 10 squamous cell carcinoma patients were collected. The images were transformed from a red–blue–green (RGB) to a hue–saturation–value (HSV) color space to obtain more meaningful color textures. By combining significant textural features (P < 0.05) in a machine learning classifier, a prediction model of malignant types was established. Results: The performance of the CAD system achieved an accuracy of 86% (19/22), a sensitivity of 90% (9/10), a specificity of 83% (10/12), a positive predictive value of 82% (9/11), and a negative predictive value of 91% (10/11) in distinguishing lung cancer types. The area under the receiver operating characteristic curve was 0.82. Conclusions: On the basis of extracted HSV textures of bronchoscopic images, the CAD system can provide recommendations for clinical diagnoses of lung cancer types.
原文英語
期刊Medical Physics
DOIs
出版狀態接受/付印 - 一月 1 2018

Keywords

  • bronchoscopy
  • color texture
  • computer-aided diagnosis
  • lung cancer

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

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