[Implementation and application of a digital cytopathological lung cancer diagnosing system].
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.
Wang
Dong
D
Cancer Center of People's Liberation Army, Nanjing 81st Hospital, Nanjing, China.
Ye
Yu-Kun
YK
Zhang
Ying
Y
Gao
Yang
Y
Yang
Yu-Bin
YB
Zhou
Yuan
Y
Guo
Ai-Yong
AY
Zhao
Bo
B
Zhu
Liang
L
Chen
Shi-Fu
SF
Shu
Zhi-Qiang
ZQ
Qian
Xue-Qing
XQ
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