Lung cancer cell identification based on artificial neural network ensembles 指導老師: 李麗華 教授 報告者: 廖偉丞

Catalog Author Abstract Keywords Introduction Artificial neural network ensembles Lung cancer diagnosis system Lung cancer cell identification Results of single artificial neural networks Results of two kinds of ensemble Neural ensemble based detection Conclusion

Author(1/1) Nanjing University Zhi-Hua Zhou、Yuan Jiang 、Yu-Bin Yang 、Shi-Fu Chen

Abstract(1/3) Neural Ensemble-based Detection (NED) is automatic pathological diagnosis procedure Built on a two-level ensemble architecture

Abstract(2/3) First-level output adenocarcinoma, squamous cell carcinoma small cell carcinoma large cell carcinoma norma

Abstract(3/3) Use NED identification rate↑ Type I error↓ reducing missing diagnoses and will save the lives of cancer patients

Keywords(1/1) Artificial neural networks Pattern recognition Image processing Computer-aided medical diagnosis Expert system

Introduction(1/1) Lung cancer is one of the most common and deadly diseases in the world. Detection of lung cancer in its early stage is the key of its cure senior pathologists are rare

Artificial neural network ensembles(1/2) Two kinds of methods Individual artificial neural networks Combining individual predictions

Artificial neural network ensembles(2/2) Simple averaging and weighted averaging Output Plurality voting Final output

Lung cancer diagnosis system(1/4) An early stage lung cancer diagnosis system named LCDS LCDS could not be clearly diagnosed by X-ray chest films The proportion of positives, i.e. lung cancer patients, is about 70–80%

Lung cancer diagnosis system(2/4) LCDS system is depicted in Fig

Lung cancer diagnosis system(3/4)

Lung cancer diagnosis system(4/4) morphologic features include the perimeter, area, roundness, and rectangleness. colorimetric features include the red component, green component, blue component, illumination, saturatio.

Lung cancer cell identification(1/1) 552 cell images(75% belong to cancer cells) Five subsets with similar size Union of four subsets as training set to train.

Results of single artificial neural networks(1/2) FANNC Learning↑ High accuracy↑

Results of single artificial neural networks(2/2) Records the average value of those five experiments. Exp1 Exp2 Exp3 Exp4 Exp5 Ave. Err (%) 48.2 44.5 45.9 41.4 47.3 45.5 Errfn (%) 19.1 14.5 20.7 15.3 17.3 17.4 Errfp (%) 20.9 18.9 16.2 21.8 19.0

Results of two kinds of ensemble(1/4) The first kind of ensemble approach

Results of two kinds of ensemble(2/4) Records the average value of those five experiments. identification↑ Exp1 Exp2 Exp3 Exp4 Exp5 Ave. Err (%) 24.5 19.1 21.6 16.2 22.7 20.8 Errfn (%) 8.2 5.5 9.0 6.3 7.3 Errfp (%) 9.1 6.4 5.4 10.0 7.6

Results of two kinds of ensemble(3/4) The second kind of ensemble approach two artificial neural network ensembles were trained, among which one ensemble was trained biased to benign diagnoses by letting negative examples dominating the training sets other ensemble was trained biased to malign diagnoses by letting positive examples dominating the training sets.

Results of two kinds of ensemble(4/4) Records the average value of those five experiments. Exp1 Exp2 Exp3 Exp4 Exp5 Ave. Err (%) 17.3 12.7 13.5 9.0 15.5 13.6 Errfn (%) 7.3 6.4 8.1 5.4 6.7 Errfp (%) 3.6 1.8 2.7 4.5 2.9

Neural ensemble based detection(1/3) NED employs a specific two-level ensemble architecture. First-level judged cancer cells Second-level Responsible to report the type of the cells.

Neural ensemble based detection(2/3) Experimental results of NED Exp1 Exp2 Exp3 Exp4 Exp5 Ave. Err (%) 15.5 10.0 11.7 8.1 12.7 11.6 Errfn (%) 3.6 1.8 2.7 Errfp (%) 5.5 4.5 6.4

Neural ensemble based detection(3/3) the flowchart of NED

Conclusion(1/1) Through adopting those techniques, NED achieves not only high rate of overall identification, but also low rate of false negative identification.

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