title: [Implementation and application of a digital cytopathological lung cancer diagnosing system].
abstract: OBJECTIVE
  To evaluate the efficacy of the digital cytopathological lung cancer diagnosing system (DCLCDS) utilizing the latest computer technologies (including reinforcement learning, image segmentation and classifier) and the cytopathological knowledge on lung cancer cells.
  
  
  METHODS
  Separate the overlapped lung cancer cells in a slice image applying the improved deBoor-Cox B-Spline algorithm; Segment cell regions in a slice image using an image segmentation algorithm based on reinforcement learning; Ensemble different classifiers, including Decision Tree classifier, Support Vector Machine (SVM) classifier and Bayesian classifier, to achieve an accurate result of cytopathological lung cancer diagnosis.
  
  
  RESULTS
  The accurate diagnosis rate for lung cancer identification of 224 images of small lung lesions aspiration biopsy from 120 cases randomly selected was 92.3%. The accurate diagnosis rate for type classification of lung cancer was 82.5%. The identification rate for abnormal nuclear cells was 71.6%.
  
  
  CONCLUSIONS
  The DCLCDS achieves a high accuracy on cytopathological lung cancer diagnosis by solving some major problems on the cytology smears, including cell overlapping, uneven coloration and impurity. It provides a relatively objective, standard tool on cytopathological lung cancer diagnosis. It has good efficacy on early diagnosis of lung cancer.
authors.affiliation: Cancer Center of People's Liberation Army, Nanjing 81st Hospital, Nanjing, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Wang
authors.forename: Yu-Kun
authors.initials: YK
authors.lastname: Ye
authors.forename: Ying
authors.initials: Y
authors.lastname: Zhang
authors.forename: Yang
authors.initials: Y
authors.lastname: Gao
authors.forename: Yu-Bin
authors.initials: YB
authors.lastname: Yang
authors.forename: Yuan
authors.initials: Y
authors.lastname: Zhou
authors.forename: Ai-Yong
authors.initials: AY
authors.lastname: Guo
authors.forename: Bo
authors.initials: B
authors.lastname: Zhao
authors.forename: Liang
authors.initials: L
authors.lastname: Zhu
authors.forename: Shi-Fu
authors.initials: SF
authors.lastname: Chen
authors.forename: Zhi-Qiang
authors.initials: ZQ
authors.lastname: Shu
authors.forename: Xue-Qing
authors.initials: XQ
authors.lastname: Qian
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003581:Cytodiagnosis / Q000379:methods; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000473:pathology; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012985:Software Design