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政大機構典藏 > 文學院 > 圖書資訊與檔案學研究所 > 期刊論文 > Item 140.119/126318

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/126318

Title:	A Machine Learning Texture Model for Classifying Lung Cancer Subtypes Using Preliminary Bronchoscopic Findings
Authors:	羅崇銘 Lo*, Chung-Ming Feng, Po-Hao Lin, Yin-Tzu
Contributors:	圖檔所
Keywords:	bronchoscopy;color texture;computer-aided diagnosis;lung cancer
Date:	2018-01
Issue Date:	2019-09-19 09:53:26 (UTC+8)
Abstract:	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.
Relation:	Medical Physics, Vol.45, No.12, pp.5509
Data Type:	article
Appears in Collections:	[圖書資訊與檔案學研究所] 期刊論文

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