1. 指導老師 : 李麗華 教授 報告者 : 廖偉丞

2. 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

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

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

5. Abstract(2/3)  First-level output Normal and Cancer cell  Second-level output adenocarcinoma, squamous cell carcinoma small cell carcinoma large cell carcinoma norma

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

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

8. 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

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

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

11. 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%

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

13. Lung cancer diagnosis system(3/4)

14. 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.

15. 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.

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

17. Results of single artificial neural networks(2/2)  Records the average value of those five experiments. Exp1Exp2Exp3Exp4Exp5Ave. Err (%) 48.244.545.941.447.345.5 Err fn (%) 19.114.520.715.317.317.4 Err fp (%) 20.917.318.916.221.819.0

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

19. Results of two kinds of ensemble(2/4)  Records the average value of those five experiments. identification↑ Exp1Exp2Exp3Exp4Exp5Ave. Err (%) 24.519.121.616.222.720.8 Err fn (%) 8.25.59.06.37.3 Err fp (%) 9.16.47.35.410.07.6

20. 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.

21. Results of two kinds of ensemble(4/4)  Records the average value of those five experiments. Exp1Exp2Exp3Exp4Exp5Ave. Err (%) 17.312.713.59.015.513.6 Err fn (%) 7.36.48.15.46.46.7 Err fp (%) 3.61.82.71.84.52.9

22. 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.

23. Neural ensemble based detection(2/3)  Experimental results of NED Exp1Exp2Exp3Exp4Exp5Ave. Err (%) 15.510.011.78.112.711.6 Err fn (%) 3.61.83.61.82.7 Err fp (%) 5.53.64.52.76.44.5

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

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

26. END