1. 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

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 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.
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

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↑
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

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.
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

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
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

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