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https://ah.lib.nccu.edu.tw/handle/140.119/126318
題名: | A Machine Learning Texture Model for Classifying Lung Cancer Subtypes Using Preliminary Bronchoscopic Findings | 作者: | 羅崇銘 Lo*, Chung-Ming Feng, Po-Hao Lin, Yin-Tzu |
貢獻者: | 圖檔所 | 關鍵詞: | bronchoscopy; color texture; computer-aided diagnosis; lung cancer | 日期: | Jan-2018 | 上傳時間: | 19-Sep-2019 | 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. | 關聯: | Medical Physics, Vol.45, No.12, pp.5509 | 資料類型: | article |
Appears in Collections: | 期刊論文 |
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