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LUNG CANCER DETECTION SYSTEM
USING NEURAL NETWORKS AND IMAGE
PROCESSING
Presented By:
H.N.Gunasinghe
AS2010379
CSC 364 1.5...
ACKNOWLEDGEMENT
This project was done By :
K.A.G. Udeshani AS2006612
Supervisors : Dr. T.G.I. Fernando
Dr. R.G.N. Meegama
...
OVERVIEW
 Introduction
 Literature review
 Methodology
 Results and discussion
 Conclusion
 Summery
AS2010379
3
H.N....
INTRODUCTION
 Lung cancer is a one of major cancers in Sri Lanka
 Still a exact treatment is not found
 Early detection...
X-Ray image
System
Nodule
Non-
Nodule
PROBLEM STATEMENT
 Aim-
 To develop a lung cancer detection system using chest
X-R...
WHAT IS A LUNG CANCER ?
 Tumors arising from cells lining the airways of the
respiratory system and it will expand into a...
OBJECTIVES
1. To develop a lung cancer detection system using
Neural Network and Image Processing.
2. To generate more acc...
MAJOR ACHIEVEMENTS
 The system was successfully built using Neural
Networks and image processing techniques
 This system...
LITERATURE REVIEW
 Digital photographs of chest X-rays and CT scans
have been used to identify lung cancer.
 Principles ...
No Other researches Lung cancer detection system using Neural
Network and Image Processing techniques
1 Computational effe...
RESEARCH QUESTIONS
 Identify the lung cancer and what are the methods that
can be used to design software solution.
 How...
METHODOLOGY (1) 49
49
Features Pixels
Results
System Overview
AS2010379
12
H.N.Gunasinghe
METHODOLOGY (2)
-SYSTEM DESIGN- Median Filtering
Sharpening
Histogram Equalization
AS2010379
13
H.N.Gunasinghe
METHODOLOGY (3)
-FEATURE EXTRACTION-
 Features extracted from an image
1. Average gray level
2. Uniformity
3. Entropy
4. ...
 This system uses a neural network with one hidden layer
containing 1000 neurons, and an output layer with 1
neuron.
pixe...
METHODOLOGY (5) – CONNECTED COMPONENT
ANALYSIS
AS2010379
16
H.N.Gunasinghe
original image median filtering Histogram equal...
RESULTS AND DISCUSSION (1)
 Results obtained when testing for the accuracy of the
system
 Two main areas that has to be ...
RESULTS AND DISCUSSION (2) – SELECT NN
pixel-based feature-based
Training graph of the neural network
R 1 0.737
MSE 1.2682...
RESULTS AND DISCUSSION (3) – RESULTS OF NN
 managed to achieve a high recognition rate for a
nodule when the neural netwo...
RESULTS AND DISCUSSION (3)
– FEATURE EXTRACTION -
20
AS2010379H.N.Gunasinghe
CONCLUSION
 This research was completed with good background
knowledge of lung cancer detection systems using
computer in...
ASSUMPTIONS AND LIMITATIONS
 Low subtlety images were used
 Any algorithm wasn’t used to avoid rib shadows
 Try the sys...
RESENT RESEARCHES
 Soft Tool Development for Characterization of Lung
Nodule from Chest X-ray Image
 International Journ...
REFERENCES
 [1] P.R. Snoeren, G.J.S. Litjens, B.V. Ginneken and N. Karssemeijer, Training a computer aided
detection syst...
THANK YOU
Q&A
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Lung cancer detection system using neural networks and image processing

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Transcript of "Lung cancer detection system using neural networks and image processing"

  1. 1. LUNG CANCER DETECTION SYSTEM USING NEURAL NETWORKS AND IMAGE PROCESSING Presented By: H.N.Gunasinghe AS2010379 CSC 364 1.5 Seminar II Department of Computer Science and Statistics , USJP
  2. 2. ACKNOWLEDGEMENT This project was done By : K.A.G. Udeshani AS2006612 Supervisors : Dr. T.G.I. Fernando Dr. R.G.N. Meegama Publication: Statistical Feature-based Neural Network Approach for the Detection of Lung Cancer in Chest X-Ray Images International Journal of Image Processing (IJIP), Volume (5) : Issue (4) : 2011 425 [link] AS2010379 2 H.N.Gunasinghe
  3. 3. OVERVIEW  Introduction  Literature review  Methodology  Results and discussion  Conclusion  Summery AS2010379 3 H.N.Gunasinghe
  4. 4. INTRODUCTION  Lung cancer is a one of major cancers in Sri Lanka  Still a exact treatment is not found  Early detection of is Lung cancer important for a successful treatment.  chest X-rays are considered to be the most widely used technique for the detection of lung cancer.  It is a complex task to analyze these images as they are projected images.  A medical expert has to make extensive knowledge of anatomy and imaging techniques. AS2010379 4 H.N.Gunasinghe
  5. 5. X-Ray image System Nodule Non- Nodule PROBLEM STATEMENT  Aim-  To develop a lung cancer detection system using chest X-Ray images as input  This is a Hybrid method- System Neural Networks Feature extraction Image processing AS2010379 5 H.N.Gunasinghe
  6. 6. WHAT IS A LUNG CANCER ?  Tumors arising from cells lining the airways of the respiratory system and it will expand into airways.  These cells are often in bright contrast in chest X- rays and take the shape of a round object.  Other diseases that can be seen in chest x-ray  Pneumonia  Tuberculosis  Lung abscess AS2010379 6 H.N.Gunasinghe
  7. 7. OBJECTIVES 1. To develop a lung cancer detection system using Neural Network and Image Processing. 2. To generate more accurate results. 3. To minimize the computational time to get an output from the system. 4. To help the people in Sri Lanka to Identify lung cancer in early stages. AS2010379 7 H.N.Gunasinghe
  8. 8. MAJOR ACHIEVEMENTS  The system was successfully built using Neural Networks and image processing techniques  This system in cooperates an effective GUI with ease of interaction AS2010379 8 H.N.Gunasinghe
  9. 9. LITERATURE REVIEW  Digital photographs of chest X-rays and CT scans have been used to identify lung cancer.  Principles of neural networks have been widely used for the detection of lung cancer in medical images with  simulated lung nodules [1, 2]  massive training artificial neural networks[4]  two level neural classifiers [5]  hybrid lung nodule detection [6]  ladder structured decision trees [3, 7] AS2010379 9 H.N.Gunasinghe
  10. 10. No Other researches Lung cancer detection system using Neural Network and Image Processing techniques 1 Computational effect is high Computational effect is less, since considering only10 features. 2 Mainly focused on one approach Two approaches were used. Pixel-based and feature-based detection with Image Processing techniques. View the suspicious areas of the lungs those can be lung nodules. 3 Use Neural Networks Use Neural Network with different architecture. 4 Not flexible This system is flexible and practical and may one can easily use this system for further improvements. RESEARCH GAP AS2010379 10 H.N.Gunasinghe
  11. 11. RESEARCH QUESTIONS  Identify the lung cancer and what are the methods that can be used to design software solution.  How doctor identifies a lung cancer using a chest X-ray.  Identify suitable methods and Image Processing techniques to extract the features from a digital image of a chest X-ray.  The optional features that can be considered as the input to the Neural Network.  Identify how Neural Network using Matlab.  Identify suitable method to design the optimum architecture of a Neural Network.  Identify the use of Graphical User Interface in Matlab. AS2010379 11 H.N.Gunasinghe
  12. 12. METHODOLOGY (1) 49 49 Features Pixels Results System Overview AS2010379 12 H.N.Gunasinghe
  13. 13. METHODOLOGY (2) -SYSTEM DESIGN- Median Filtering Sharpening Histogram Equalization AS2010379 13 H.N.Gunasinghe
  14. 14. METHODOLOGY (3) -FEATURE EXTRACTION-  Features extracted from an image 1. Average gray level 2. Uniformity 3. Entropy 4. Standard deviation 5. Skewness  Tackled noise removed by preprocessing image  Most projects used integrated preprocessing techniques to extract the lung region  Extracted region was used to apply for further image processing techniques. 6. Smoothness 7. Contrast 8. Homogeneity 9. Energy 10. correlation AS2010379 14 H.N.Gunasinghe
  15. 15.  This system uses a neural network with one hidden layer containing 1000 neurons, and an output layer with 1 neuron. pixel-based intensity input vectors - purelin and tansig transfer functions feature-based inputs vectors - two tansig transfer functions METHODOLOGY (4) -NEURAL NETWORK - BACKPROPAGATION AS2010379 15 H.N.Gunasinghe
  16. 16. METHODOLOGY (5) – CONNECTED COMPONENT ANALYSIS AS2010379 16 H.N.Gunasinghe original image median filtering Histogram equalization Labeling connected componentsbinary threshold image
  17. 17. RESULTS AND DISCUSSION (1)  Results obtained when testing for the accuracy of the system  Two main areas that has to be considered 1. Neural network a. Pixel based technique (2401 pixels) b. Feature based technique (10 features) 2. Connected component analysis a. View suspicious areas b. Calculate the roundness of the connected components AS2010379 17 H.N.Gunasinghe
  18. 18. RESULTS AND DISCUSSION (2) – SELECT NN pixel-based feature-based Training graph of the neural network R 1 0.737 MSE 1.2682e-009 0.2684 AS2010379 18 H.N.Gunasinghe
  19. 19. RESULTS AND DISCUSSION (3) – RESULTS OF NN  managed to achieve a high recognition rate for a nodule when the neural network was trained using pixel-based intensity values.  Recognizing a non-nodule was 16% lower with statistical feature-based training of the neural network. AS2010379 19 H.N.Gunasinghe
  20. 20. RESULTS AND DISCUSSION (3) – FEATURE EXTRACTION - 20 AS2010379H.N.Gunasinghe
  21. 21. CONCLUSION  This research was completed with good background knowledge of lung cancer detection systems using computer intelligence  The detection rate of  Feature based technique – 88%  Pixel based technique – 96%  Successfully developed a solution using Neural Networks and image processing techniques with a GUI  A user has only to select the digital chest x ray as input and system will show suspicious areas of the chest x ray and the presence of lung nodules  It is considered only the visible area of the chest x – ray for the nodule detection AS2010379 21 H.N.Gunasinghe
  22. 22. ASSUMPTIONS AND LIMITATIONS  Low subtlety images were used  Any algorithm wasn’t used to avoid rib shadows  Try the system with many preprocessing techniques  Develop lung region segmentation algorithm to use with many databases.  Try with different NN architectures  Develop algorithms to overcome rib shadows.  Apply these techniques to identify other cancers 22 AS2010379H.N.Gunasinghe FUTURE WORKS
  23. 23. RESENT RESEARCHES  Soft Tool Development for Characterization of Lung Nodule from Chest X-ray Image  International Journal of Image Processing and Vision Sciences ISSN (Print): 2278 – 1110, Volume-2, Issue-1, 2012 [link]  Feature Extraction and Principal Component Analysis for Lung Cancer Detection in CT scan Images  Ada et al., International Journal of Advanced Research in Computer Science and Software Engineering 3(3), March - 2013, pp. 187-190 [link]  Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods  Chang et al. BMC Bioinformatics 2013, 14:170 [link] AS2010379 23 H.N.Gunasinghe
  24. 24. REFERENCES  [1] P.R. Snoeren, G.J.S. Litjens, B.V. Ginneken and N. Karssemeijer, Training a computer aided detection system with simulated lung nodules in chest radiographs, Proc. 3rd International Workshop on Pulmonary Image Analysis, Beijing, 2010.  [2] G. Coppini, S. Diciotti, M. Falchini, N. Villari and G. Valli, Neural networks for computer aided diagnosis: detection of lung nodules in chest radiograms, IEEE Trans. On Information Technology in Biomedicine, vol. 4, pp. 344-357, 2003.  [3] M.G. Penedo, M.J. Carreira, A. Mosquera and D. Cabello, Computer aided diagnosis: A neural network based approach to lung nodule detection, IEEE Trans. on Medical Imaging, vol. 17, N 6. pp. 872-880, 1998.  [4] K. Suzuki, J. Shiraishi, H. Abe, H. MacMahon and K. Doi, False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network, Academi Radiology, vol. 12, N 2, pp. 191-201, 2003.  [5] J.S. Lin, S.B. Lo, A. Hasegawa, M.T. Freedman and S.K. Mun, Reduction of false  positives in lung nodule detection using a two-level neural classification, IEEE Trans. On Medical Imaging, vol. 15, pp. 206-216, 1996.  [6] Y.S.P. Chiou, Y.M.F. Lure and P.A. Ligomenides, Neural networks image analysis and classification in hybrid lung nodule detection (HLND) system, IEEE Workshop on Neural Networks for Signal Processing, pp. 517-526, 1993.  [7] D.H. Ballard and J. Sklansky, A ladder-structured decision tree for recognizing tumors in chest radiographs, IEEE Trans. on Computers, vol. C-25, pp. 503-513, 1976. AS2010379 24 H.N.Gunasinghe
  25. 25. THANK YOU Q&A
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