| dc.contributor | 圖檔所 | - |
| dc.creator (作者) | 羅崇銘 | - |
| dc.creator (作者) | Lo*, Chung-Ming | - |
| dc.creator (作者) | Feng, Po-Hao | - |
| dc.creator (作者) | Lin, Yin-Tzu | - |
| dc.date (日期) | 2018-01 | - |
| dc.date.accessioned | 19-Sep-2019 09:53:26 (UTC+8) | - |
| dc.date.available | 19-Sep-2019 09:53:26 (UTC+8) | - |
| dc.date.issued (上傳時間) | 19-Sep-2019 09:53:26 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/126318 | - |
| dc.description.abstract (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. | - |
| dc.format.extent | 108 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Medical Physics, Vol.45, No.12, pp.5509 | - |
| dc.subject (關鍵詞) | bronchoscopy; color texture; computer-aided diagnosis; lung cancer | - |
| dc.title (題名) | A Machine Learning Texture Model for Classifying Lung Cancer Subtypes Using Preliminary Bronchoscopic Findings | - |
| dc.type (資料類型) | article | - |
