Lung Cancer Detection Model Using Convolution Neural Network and Fuzzy Clustering Algorithms

International Journal of Computer Trends and Technology (IJCTT)          
© 2019 by IJCTT Journal
Volume-67 Issue-11
Year of Publication : 2019
Authors : P. Prakashbabu, D. Ashok Kumar, T.Vithyaa
DOI :  10.14445/22312803/IJCTT-V67I11P104


MLA Style:P. Prakashbabu, D. Ashok Kumar, T.Vithyaa "Lung Cancer Detection Model Using Convolution Neural Network and Fuzzy Clustering Algorithms," International Journal of Computer Trends and Technology 67.11 (2019):18-27.

APA Style P. Prakashbabu, D. Ashok Kumar, T.Vithyaa. Lung Cancer Detection Model Using Convolution Neural Network and Fuzzy Clustering Algorithms International Journal of Computer Trends and Technology, 67(11),18-27.

This paper discusses the formation of Lung cancer detection system by using the techniques of Image processing. The system formed can take any type of medical image within the three choices consisting of CT, MRI and Ultrasound images. Here the proposed model is developed using Fuzzy-CMeans and Convolution Neural Network (CNN) algorithm used for feature selection. This paper is an extension of image processing using lung cancer detection and produces the results of feature extraction and feature selection after segmentation. The system formed accepts any one of medical image within the three choices consisting of MRI, CT and Ultrasound image as input. After preprocessing of image, wiener filter is used for remove noise and unwanted region. This present work proposes a method to detect the cancerous cells effectively from the CT, MRI scan and Ultrasound images. Pixel Segmentation has been used for FCM segmentation and filter is used for De-noising the medical images. Simulation results are obtained for the cancer detection system using MATLAB and comparison is done between normal lung and abnormallung medical images.

[1] W.J. Choi and T.S. Choi, “Automated pulmonary nodule detection system in computed tomography images: A hierarchical block classification approach,” Entropy, vol. 15, no. 2, pp. 507–523, 2013.
[2] A. Chon, N. Balachandar, and P. Lu, “Deep convolutional neural networks for lung cancer detection,” tech. rep., Stanford University, 2017.
[3] Y. LeCun, K. Kavukcuoglu, and C. Farabet, “Convolutional networks and applications in vision.,” in Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), pp. 253–256, IEEE, 2010.
[4] K. Alex, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems 25 (NIPS 2012) (F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, eds.), pp. 1097–1105, 2012.
[5] H. Suk, S. Lee, and D. Shen, “Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis,” NeuroImage, vol. 101, pp. 569–582, 2014.
[6] G. Wu, M. Kim, Q. Wang, Y. Gao, S. Liao, and D. Shen, “Unsupervised deep feature learning for deformable registration of mr brain images.,” Medical Image Computing and Computer-Assisted Intervention, vol. 16, no. Pt 2, pp. 649–656, 2013.
[7] Y. Xu, T. Mo, Q. Feng, P. Zhong, M. Lai, and E. I. Chang, “Deep learning of feature representation with multiple instance learning for medical image analysis,” in IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, pp. 1626–1630, 2014.
[8] D. Kumar, A. Wong, and D. A. Clausi, “Lung nodule classification using deep features in ct images,” in 2015 12th Conference on Computer and Robot Vision, pp. 133– 138, June 2015.
[9] Y. Bar, I. Diamant, L. Wolf, S. Lieberman, E. Konen, and H. Greenspan, “Chest pathology detection using deep learning with non-medical training,” Proceedings - International Symposium on Biomedical Imaging, vol. 2015-July, pp. 294–297, 2015. Sample Dataset TP TN FP FN Sensitivity Specificity Accuracy Training dataset 968 842 10 12 104 89% 46% 88% Testing/ Validation 419 376 5 7 31 93% 42% 91% Average 89% International Journal of Computer Trends and Technology (IJCTT) – Volume 67 Issue 11 - November 2019 ISSN: 2231-2803 Page 27
[10] W. Sun, B. Zheng, and W. Qian, “Computer aided lung cancer diagnosis with deep learning algorithms,” in SPIE Medical Imaging, vol. 9785, pp. 97850Z–97850Z, International Society for Optics and Photonics, 2016.
[11] J. Tan, Y. Huo, Z. Liang, and L. Li, “A comparison study on the effect of false positive reduction in deep learning based detection for juxtapleural lung nodules: Cnnvsdnn,” in Proceedings of the Symposium on Modeling and Simulation in Medicine, MSM ?17, (San Diego, CA, USA), pp. 8:1–8:8, Society for Computer Simulation International, 2017.
[12] R. Golan, C. Jacob, and J. Denzinger, “Lung nodule detection in ct images using deep convolutional neural networks,” in 2016 International Joint Conference on Neural Networks (IJCNN), pp. 243–250, July 2016.
[13] AjalaFunmilola A, Oke O.A, Adedeji T.O, Alade O.M, Oyo Adewusi E.A, ?Fuzzy k-c-means Clustering Algorithm for Medical Image Segmentation?, Journal of Information Engineering and Applications, ISSN 2224-5782 (print) ISSN 2225-0506 (online), Vol 2, No.6, 2012.
[14] Christian D., Naoufel W., Fatma T., Hussain, "Cell Extraction from Sputum Images for Early lung Cancer Detection", IEEE 978-1-4673-0784-0/12, 2012 .
[15] Mokhled S. AL-TARAWNEH, ?Lung Cancer Detection Using Image Processing Techniques?, Leonardo Electronic Journal of Practices and Technologies, ISSN 1583-1078, Issue 20, January-June 2012.
[16] SajithKecheril S, D Venkataraman, J Suganthi and K Sujathan, "Segmentation of Lung Glandular Cells using Multiple Color Spaces", International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.3, June 2012 .
[17] Tseng L, Huang L, “An Adaptive Thresholding Method for Automatic Lung Segmentation in CT Images”, IEEE AFRICON,pp 1-5, September 23-25, 2009.
[18] Chaudhary A, Singh S S, “Lung Cancer Detection Using Digital Image Processing”, IJREAS vol 2, 1351-1359, Issue 2, 2012.
[19] Abdullah A A and Mohamaddiah H, “Development of Cellular Neural Network Algorithm for Detecting Lung Cancer Symptoms”, IEEE EMBS Conference on Biomedical Engineering & Sciences, 138- 143, 2010.
[20] Kaggle, “Data science bowl 2017.”, 2017.
[21] LUNA16, “Lung nodule analysis 2016.”, 2017.

Lung cancer,lung segmentation,Fuzzy-CMeans, CNN, Feature extraction.