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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/126320


    Title: Classification of Lung Cancer Subtypes Based on Autofluorescence Bronchoscopic Pattern Recognition: A Preliminary Study
    Authors: 羅崇銘
    Lo*, Chung-Ming
    Feng, Po-Hao
    Chen, Tzu-Tao
    Lin, Yin-Tzu
    Chiang, Shang-Yu
    Contributors: 圖檔所
    Keywords: Lung cancer;Autofluorescent bronchoscopy;Computer-aided diagnosis;Color texture
    Date: 2018-06
    Issue Date: 2019-09-19 09:53:38 (UTC+8)
    Abstract: 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.
    Relation: Computer Methods and Programs in Biomedicine, Vol.163, pp.33
    Data Type: article
    DOI 連結: https://doi.org/10.1016/j.cmpb.2018.05.016
    DOI: 10.1016/j.cmpb.2018.05.016
    Appears in Collections:[圖書資訊與檔案學研究所] 期刊論文

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