Early detection of cancer is the most promising way to enhance a patient's chance for survival. This paper presents a computer-aided classification method using computed tomography (CT) images of the lung based on ensemble of three classifiers including MLP, KNN and SVM. In this study, the entire lung is first segmented from the CT images and specific features like Roundness, Circularity, Compactness, Ellipticity, and Eccentricity are calculated from the segmented images. These morphological features are used for classification process in a way that each classifier makes its own decision. Finally, majority voting method is used to combine decisions of this ensemble system. The performance of this system is evaluated using 60 CT scans collected by Lung Image Database Consortium (LIDC) and the results show good improvement in diagnosing of pulmonary nodules.

1. Lung Nodule Diagnosis from CT
Images Based on Ensemble Learning
Mohammad Hossein Fazel Zarandi
Farzad Vasheghani Farahani
Abbas Ahmadi
2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology
Niagara Falls, Canada | August 12 - 15

2. Topics
• Objective
• Procedure
• Background
• Proposed system
• Contribution
• Conclusion
• References

3. Objective
The purpose of this study is to “design a CAD system for diagnosis
of lung cancer” which is:
• Helping physicians as a second opinion
• Fast and accurate for early diagnosis of this disease
• Based on processing CT images and image analysis techniques
• Using ensemble of classifiers

4. Procedure
How to achieve the desired objectives?

5. Background

6. About lung cancer
• Cancer is one of top mortal diseases in the world [1] like
heart diseases and diabetes.
• Lung cancer is the most leading cause of deaths in both
women and men (27.2% of all cancer deaths) [2].
• Lung cancer is caused by an uncontrollable irregular
growth of cells in lung tissue.
• Lung tissue abnormalities that are roughly spherical with
round opacity and a diameter of up to 30mm [3] are
known as lung nodules.

7. Computer-aided diagnosis (CAD) systems
• CAD systems Are very helpful for radiologists in the detection and
diagnosing abnormalities earlier and faster [4].
• Indeed, computer-aided
diagnosis systems act as
a second opinion for
radiologists before
suggesting a biopsy test
[5].

8. Machine learning methods
• Ensemble learners combine decisions
of base classifiers to form an
integrated output [8].
• Machine learning methods have become one of the dominant
approaches in CAD for medical imaging [6].
• One of the four pioneer branches in the field of machine learning is
ensemble based systems [7].

9. Proposed System

10. Systematic overview
Image
Processing
Classification

11. Lung CT dataset
• 60 CT images including both men and women collected by Lung
Image Database Consortium (LIDC).
• Lung lesions in these images are marked by up to four
radiologists in VIA group.
• All the lung CT images are in the
format of DICOM with the size of
512*512.

12. Image processing phase
Image processing module in this model consists of 4 main steps:
Preprocessing
Segmentation
Post
processing
Feature
extraction
Raw images
Extracted
features

13. 1) Preprocessing
• Image preprocessing is a process
which eliminates primary noise and
image distortion, and also enhances
important features exists in CT
images.
• By using some image enhancement
methods including median filter
and histogram equalization.

14. 2) Segmentation
a) Background elimination and other additional parts such as bones to
extract lung tissue and region of interest (ROI) in the lung image.
b) Convert the image to black and white in order to prepare it for
boundary tracing.
Method: Region growing
Method: Thresholding

15. 2) Segmentation (Cont.)
Background elimination
using region growing
Convert to black and
white using thresholding

16. 3) Post processing
• Eliminate unwanted objects (small or
large) inside and outside the binary
image of lungs.
• Join the detected object in the image
together by filling in the gaps between
them and by smoothing their outer
edges.
• Fill image regions and holes of the
detected objects.
• Find boundaries of the objects by
concentrating only on exterior
boundaries.

17. 4) Feature extraction
In order to classify suspected objects as nodule or non-nodule,
further working is needed to extract specific features from raw
images.
Feature Definition
Roundness
4𝐴
𝜋𝐿2
Circularity
4𝜋𝐴
𝑃2
Compactness 𝑃2
4𝜋𝐴
Ellipticity 𝜋𝐿2
2𝐴
Eccentricity −
• In this study, five morphological
features are extracted for each
suspected objects from CT images.
• P, A and L denote the perimeter, area
and maximum diameter of the
suspected objects, respectively.

18. Classification phase
One of the suitable methods to improve the accuracy of
classification is the use of multiple classifier systems.
Multiple classifier systems generally
consist of two main parts [9]:
• Creation an ensemble
• Combination of class labels

19. 1) Creating an ensemble
Three different classifiers including Multilayer perceptron (MLP),
K-Nearest neighbor (KNN), and Support vector machines (SVM)
work on numerical data obtained from feature vectors.
Note: The extracted features from suspected
objects are considered as inputs to the ensemble
system and they are normalized between zero and
one. Hence, the feature set consists of five
extracted features from the previous section and a
binary target feature that defines suspected
objects are nodule or not (0 or 1).

20. 2) Combining outputs
Each base classifier has its own opinion and identifies suspected
objects as nodule or non-nodule.
Majority voting techniques is used to combine the results of base
classifiers.
Final output of
ensemble system

21. System Performance Measurements
Accuracy (Acc), Sensitivity (Sn) , Specificity (Sp), G-mean , Precision
(Prc) and F-measure of the proposed system:
Proposed
method
System Performance Measurements
Acc Sn Sp G-mean Prc F-measure
MLP 90.41 73.55 94.68 83.45 77.76 75.60
KNN 91.20 81.76 93.59 87.48 76.33 78.96
SVM 90.60 73.44 94.94 83.50 78.59 75.93
Majority
Voting
94.65 84.16 97.30 90.49 88.76 86.40

22. Confusion matrix
MLP
Actual Predicted
Negative Positive
Negative 75.56 % 4.25 %
Positive 5.34 % 14.85 %
KNN
Actual Predicted
Negative Positive
Negative 74.69 % 5.12 %
Positive 3.68 % 16.51 %
SVM
Actual Predicted
Negative Positive
Negative 75.77 % 4.04 %
Positive 5.36 % 14.83 %
Proposed method
Actual Predicted
Negative Positive
Negative 77.66 % 2.15 %
Positive 3.20 % 16.99 %

23. Contribution
The main contribution of this paper is to:
present a classifier ensemble system that uses image processing
and data mining techniques for lung nodule identification.
• Feature extraction of suspected objects in CT images is the main
goal of image processing phase.
• In data mining phase, there are three different classifiers including
MLP, KNN and SVM that work on extracted features to identify lung
objects as nodule or non-nodule in the form of group decision-
making.

24. Conclusion
• In present work, the manner of a CAD system in diagnosis process is
simulated.
• Our proposed CAD system is a highly promising method, providing high
performance in the identification of pulmonary nodules from CT images.
• For the sake of checking the effectiveness of the proposed hybrid
intelligent method, 5-fold cross-validation approach was applied and the
program ran for 10 times.
• Performance measurements show that the system could be used as a
second opinion for physicians in the diagnosis of lung cancer from CT
images.

25. References
1. “WHO | The top 10 causes of death.”
2. “Cancer of the Lung and Bronchus - SEER Stat Fact Sheets.” [Online]. Available:
http://seer.cancer.gov/statfacts/html/lungb.html. [Accessed: 13-Feb-2015].
3. J. H. M. Austin, P. Naidich, L. Muller, M. Hansell, and A. Zerhouni, “Glossary of terms for CT of the lungs:
recommendations of the Nomenclature Committee of the Fleischner Society,” Radiology, vol. 200, no. 2, pp.
327–331, 1996.
4. R. N. Strickland, “Tumor detection in non stationary backgrounds,” IEEE Trans. Med. Imaging, vol. 13, no. 3, pp.
491–9, Jan. 1994.
5. S. B. Lo, S. A. Lou, J. Lin, M. T. Freedman, M. V Chien, and S. K. Mun, “Artificial Convolution Neural Network
Techniques and Applications for Lung Nodule Detection,” Med. Imaging, IEEE Trans., vol. 14, no. 4, pp. 711–718,
1995.
6. S. L. a. Lee, a. Z. Kouzani, and E. J. Hu, “Automated identification of lung nodules,” 2008 IEEE 10th Work.
Multimed. Signal Process., pp. 497–502, Oct. 2008.
7. T. G. Dietterich, “Machine-Learning Research,” AI Mag., vol. 18, no. 4, pp. 97–136, 1997.
8. S. L. a Lee, a Z. Kouzani, and E. J. Hu, “Random forest based lung nodule classification aided by clustering.,”
Comput. Med. Imaging Graph., vol. 34, no. 7, pp. 535–42, Oct. 2010.
9. R. Polikar, “Ensemble Based Systems in Decision Making,” Circuits Syst. Mag. IEEE, vol. 6, no. 3, pp. 21–45,
2006.

26. Thanks for listening …