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題名 Classification of Lung Cancer Subtypes Based on Autofluorescence Bronchoscopic Pattern Recognition: A Preliminary Study
作者 羅崇銘
Lo*, Chung-Ming
Feng, Po-Hao
Chen, Tzu-Tao
Lin, Yin-Tzu
Chiang, Shang-Yu
貢獻者 圖檔所
關鍵詞 Lung cancer ; Autofluorescent bronchoscopy ; Computer-aided diagnosis ; Color texture
日期 2018-06
上傳時間 19-九月-2019 09:53:38 (UTC+8)
摘要 Background and objectives
     Lung cancer is the leading cause of cancer deaths worldwide. With current use of autofluorescent bronchoscopic imaging to detect early lung cancer and limitations of pathologic examinations, a computer-aided diagnosis (CAD) system based on autofluorescent bronchoscopy was proposed to distinguish different pathological cancer types to achieve objective and consistent diagnoses.
     
     Methods
     The collected database consisted of 12 adenocarcinomas and 11 squamous cell carcinomas. The corresponding autofluorescent bronchoscopic images were first transformed to a hue (H), saturation (S), and value (V) color space to obtain better interpretation of the color information. Color textural features were respectively extracted from the H, S, and V channels and combined in a logistic regression classifier to classify malignant types by machine learning.
     
     Results
     After feature selection, the proposed CAD system achieved an accuracy of 83% (19/23), a sensitivity of 73% (8/11), a specificity of 92% (11/12), a positive predictive value of 89% (8/9), a negative predictive value of 79% (11/14), and an area under the receiver operating characteristic curve of 0.81 for distinguishing lung cancer types.
     
     Conclusions
     The proposed CAD system based on color textures of autofluorescent bronchoscopic images provides a diagnostic method of malignant types in clinical use.
關聯 Computer Methods and Programs in Biomedicine, Vol.163, pp.33
資料類型 article
DOI https://doi.org/10.1016/j.cmpb.2018.05.016
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) Chen, Tzu-Tao-
dc.creator (作者) Lin, Yin-Tzu-
dc.creator (作者) Chiang, Shang-Yu-
dc.date (日期) 2018-06-
dc.date.accessioned 19-九月-2019 09:53:38 (UTC+8)-
dc.date.available 19-九月-2019 09:53:38 (UTC+8)-
dc.date.issued (上傳時間) 19-九月-2019 09:53:38 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/126320-
dc.description.abstract (摘要) Background and objectives
     Lung cancer is the leading cause of cancer deaths worldwide. With current use of autofluorescent bronchoscopic imaging to detect early lung cancer and limitations of pathologic examinations, a computer-aided diagnosis (CAD) system based on autofluorescent bronchoscopy was proposed to distinguish different pathological cancer types to achieve objective and consistent diagnoses.
     
     Methods
     The collected database consisted of 12 adenocarcinomas and 11 squamous cell carcinomas. The corresponding autofluorescent bronchoscopic images were first transformed to a hue (H), saturation (S), and value (V) color space to obtain better interpretation of the color information. Color textural features were respectively extracted from the H, S, and V channels and combined in a logistic regression classifier to classify malignant types by machine learning.
     
     Results
     After feature selection, the proposed CAD system achieved an accuracy of 83% (19/23), a sensitivity of 73% (8/11), a specificity of 92% (11/12), a positive predictive value of 89% (8/9), a negative predictive value of 79% (11/14), and an area under the receiver operating characteristic curve of 0.81 for distinguishing lung cancer types.
     
     Conclusions
     The proposed CAD system based on color textures of autofluorescent bronchoscopic images provides a diagnostic method of malignant types in clinical use.		-
dc.relation (關聯) Computer Methods and Programs in Biomedicine, Vol.163, pp.33-
dc.subject (關鍵詞) Lung cancer ; Autofluorescent bronchoscopy ; Computer-aided diagnosis ; Color texture-
dc.title (題名) Classification of Lung Cancer Subtypes Based on Autofluorescence Bronchoscopic Pattern Recognition: A Preliminary Study-
dc.type (資料類型) article-
dc.identifier.doi (DOI) 10.1016/j.cmpb.2018.05.016-
dc.doi.uri (DOI) https://doi.org/10.1016/j.cmpb.2018.05.016-