Lung Lesion Classification Scheme Using Optimization Techniques and Hybrid (KNN-SVM) Classifier

References

• A. B. Mathews, and M. K. Jeyakumar, “Segmentation of lung images for tumor detection using color based k-means clustering algorithm,” J. Int. Pharm. Res., Vol. 46, no. 1, pp. 191–197, 2019.
   Google Scholar
• A. B. Mathews, and M. K. Jeyakumar, “A novel ASNO segmentation technique for segmenting the JUXTA vascular region,” Int. J. Eng.Technol., Vol. 7, no. 3.27, pp. 47–52, 2018. doi: 10.14419/ijet.v7i3.27.17652
   Google Scholar
• S. Dabade, S. Chaudhari, S. Jadhav, and A. Nichal. “A review paper on computer aided system for lung cancer detection”, 2017 International Conference on Big Data, IoT and Data Science (BID), 2017.
   Google Scholar
• D. Sharma, and G. Jindal, “Identifying lung cancer using image processing techniques,” Int. Conf. Comput. Tech. Artif. Intell., 116–120, 2011.
   Google Scholar
• N. Homma, “CT Image based computer-aided lung cancer diagnosis,” in Theory and applications of CT imaging and analysis, N. Homma Ed., 2011, ISBN: 978-953-307-234.
   Google Scholar
• R. Naseem, K. S. Alimgeer, and T. Bashir, “Recent trends in computer aided diagnosis of lung nodules in thorax CT Scans,” IEEE 2017.
   Google Scholar
• G. J. Amir, and H. P. Lehmann, “After detection: the improved accuracy of lung cancer assessment using radiologic computer-aided diagnosis,” Acad. Radiol., Vol. 23, pp. 186–191, 2016. doi: 10.1016/j.acra.2015.10.014
   PubMed Web of Science ®Google Scholar
• H. P. Chan, L. Hadjiiski, C. Zhou, and B. Sahiner, “Computer-aided diagnosis of lung cancer and pulmonary embolism in computed tomography—A review,” Acad. Radiol., Vol. 15, pp. 535–555, 2008. doi: 10.1016/j.acra.2008.01.014
   PubMed Web of Science ®Google Scholar
• T. Messay, R. C. Hardie, and S. K. Rogers, “A new computationally efficient CAD system for pulmonary nodule detection in CT imagery,” Med. Image Anal., Vol. 14, pp. 390–406, 2010. doi: 10.1016/j.media.2010.02.004
   PubMed Web of Science ®Google Scholar
• K. Awai, K. Murao, A. Ozawa, M. Komi, H. Hayakawa, S. Hori, and Y. Nishimura, “Pulmonary nodules at chest CT: Effect of computer-aided diagnosis on radiologists detection performance,” Radiology, Vol. 230, no. 2, pp. 347–352, 2004. doi: 10.1148/radiol.2302030049
   PubMed Web of Science ®Google Scholar
• N. Jaspinder Kaur, and D. Kaur, “A survey of lung cancer detection techniques on CT scan images,” Int. J. Sci.Eng. Res. 2014.
   Google Scholar
• K. Vijila Rani, and S. Joseph Jawhar, “Novel technology for lung tumor detection using nanoimage,” IETE. J. Res. 2019, pp. 1–15. Impact Factor: 0.829. doi: 10.1080/03772063.2019.1654935
   Google Scholar
• D. E. Midthun, “Early detection of lung cancer,” F1000Res, Vol. 5, pp. 739, 2016. doi: 10.12688/f1000research.7313.1
   Google Scholar
• J. M. Kuhnigk, H. Hahn, M. Hindennach, V. Dicken, S. Krass, and H. O. Peitgen, “Pulmonary lobe segmentation by anatomy-guided 3-DWatershed transform,” Proc. SPIE Med. Imag., Vol. 5032, pp. 1482–1490, 2003. doi: 10.1117/12.480321
   Google Scholar
• X. Zhou, T. Hayashi, T. Hara, H. Fujita, R. Yokoyama, T. Kiryu, and H. Hoshi, “Automatic recognition of pulmonary lobes and fissures from multi-slice CT images,” Proc. SPIE Med. Imag., Vol. 5370, pp. 1629–1633, 2004. doi: 10.1117/12.534499
   Google Scholar
• R. Golan, C. Jacob, and J. Denzinger, “Lung nodule detection in CT images using deep convolutional neural networks,” IEEE 2016.
   Google Scholar
• M. Gong, Y. Liang, J. Shi, W. Ma, and J. Ma, “Fuzzy C-means clustering with local Information and kernel metric for image segmentation,” IEEE Trans. Image Process., Vol. 22, pp. 573–584, 2013. doi: 10.1109/TIP.2012.2219547
   PubMed Web of Science ®Google Scholar
• K. Sankar, and M. Prabhakaran, “An improved architecture for lung cancer cell identification using gabor filter and intelligence system,” Int. J. Eng. Sci., Vol. 2, pp. 38–43, 2013.
   Google Scholar
• T. K. Mansoori, A. Suman, and S. K. Mishra, “Application of genetic algorithm for cancer diagnosis by feature selection,” Int. J. Eng. Res. Technol. (IJERT), Vol. 3, pp. 1295–1300, 2014.
   Google Scholar
• M. M. Theresa, and V. S. Bharathi, “Computer aided diagnostic (CAD) for feature extraction of lungs in chest radiograph using different transform feature,” Biomed. Res., 1–8, 2017.
   PubMed Web of Science ®Google Scholar
• T. W. Way, B. Sahiner, H. P. Chan, L. Hadjiiski, P. N. Cascade, A. Chughtai, N. Bogot, and E. Kazerooni, “Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features,” Med. Phys., Vol. 36, pp. 3086–3098, 2009. doi: 10.1118/1.3140589
   PubMed Web of Science ®Google Scholar
• A. P. Reeves, A. B. Chan, D. F. Yankelevitz, C. I. Henschke, B. Kressler, and W. J. Kostis, “On measuring the change in size of pulmonary nodules,” IEEE Trans. Med. Imaging, Vol. 25, pp. 435–450, 2006. doi: 10.1109/TMI.2006.871548
   PubMed Web of Science ®Google Scholar
• T. Zhou, H. Lu, J. Zhang, and H. Shi. Pulmonary nodule detection model based on SVM and CT image feature-level fusion with rough sets. China: Hindawi Publishing Corporation BioMed Research International, 2016 , pp. 1–13.
   Google Scholar
• Y. Yim, H. Hong, and Y. G. Shin, “Hybrid lung segmentation in chest CT images for computer-aided diagnosis,” IEEE 2005.
   Google Scholar
• A. O. De CarvalhoFilho, A. C. Silva, A. C. de Paiva, R. A. Nunes, and M. Gattass, “Computer-aided diagnosis system for lung nodules based on computed tomography using shape analysis, a genetic algorithm and SVM”,” Med. Biol. Eng. Comput., 1–8, 2016.
   Web of Science ®Google Scholar
• R. Li, W.S Ye, and Z. Shi, “SVM-KNN classifier-a new method of improving the accuracy of SVM classifier,” Chin. J. Electron., Vol. 30, no. 5, pp. 745–748, 2005.
   Google Scholar
• K. Vijila Rani, and S. Joseph Jawhar, “Novel method for lung tumour detection using wavelet shrinkage-based double classifier analysis,” IETE. J. Res. 2018. Impact Factor: 0.829
   Web of Science ®Google Scholar
• S. Singh, and I. M. SajinSanju, “The detection of pulmonary nodules in CT images using heuristic approach segmentation and classification,” ARPN J. Eng. Appl.Sci., Vol. 11, no. 9, pp. 5916–5920, May 2016.
   Google Scholar
• A. Pathan, and B. K. Saptalkar, “'Detection and classification of lung cancer using artificial neural network’,” Int. J. Adv. Comput. Eng. Commun. Technol., Vol. 1, pp. 62–67, 2011.
   Google Scholar
• N. Sahu, and V. Jain, “Improved content based texture image classification using cascade RBF,” Int. J. Software Web Sci. (IJSWS, Vol. 5, pp. 83–87, 2013.
   Google Scholar
• S. Logesh kumar, M. Swathy, S. Sathish, J. Sivaraman, and M. Rajasekar, “Identification of lung cancer cell using watershed segmentation on CT images,” Indian J. Sci. Technol., Vol. 9, no. 1, January 2016. doi:10.17485/ijst/2016/v9i1/85765
   Google Scholar
• O. Elsayed, K. Mahar, M. Kholief, and H. A. Khater. “Automatic detection of the pulmonary nodules from CT Images”, SAI Intelligent Systems Conference 2015.
   Google Scholar
• S. Kushwaha, and P. Maya, “Analysis of lung nodule and classification based on SVM classifier and fuzzy C-means clustering for effective segmentation,” Int. J. Digital Appl. Contemp., Vol. 4, no. 8, March 2016.
   Google Scholar
• Q. Abbas, “Lung-deep: a computerized tool for detection of lung nodule patterns using deep learning algorithms,” (IJACSA) Int. J. Adv. Comput. Sci. Appl., Vol. 8, no. 10, 2017.
   Google Scholar
• L. Wu, J. Z. Cheng, S. Li, B. Lei, T. Wang, and D. Ni, “FUIQA: Fetal ultrasound image quality assessment with deep convolutional networks,” IEEE Transactions on Cybernetics, Vol. 47, pp. 1336–1349, 2017. doi: 10.1109/TCYB.2017.2671898
   PubMed Web of Science ®Google Scholar
• V. Krishnaiah, G. Narsimha, and N. Subhash Chandra, “Diagnosis of lung cancer prediction system using data mining classification techniques,” Int. J. Comput. Sci. Inf. Technol., Vol. 4, pp. 39–45, 2013.
   Google Scholar
• A. Mangalakshmi, G. Saranya, I. Sobana Devi, and V. Syamala Devi, “Lung nodule classification using deep features in CT images”,” Int. J.Adv. Res. Comput. Sci. Software Eng., Vol. 6, no. 3, March 2016.
   Google Scholar
• V. C. Patil, and S. R. Dhotre, “Lung cancer detection from images of computer tomography scan,” Int. J. Adv. Res. Comput. Commun. Eng. ISO 3297:2007 Certified, Vol. 5, no. 7, July 2016.
   Google Scholar
• Y. Lee, T. Hara, H. Fujita, S. Itoh, and T. Ishigaki, “Automated detection of pulmonary nodules in helical CT images based on an improved template matching technique,” IEEETrans.Med.Imag, Vol. 20, no. 7, pp. 595–604, Jul. 2001.
   PubMed Web of Science ®Google Scholar
• C. L. Jiang, S. W. He, Y. D. Zhang, H. X. Duan, T. Huang, Y. C. Huang, G. F. Li, L. J. Ma, G. B. Zhou, and Y. Cao, “Air pollution and DNA methylation alterations in lung cancer,” Oncotarget., Vol. 8, pp. 1369–1391, 2017. doi: 10.18632/oncotarget.13622
   PubMedGoogle Scholar
• S. Saita, M. Kubo, Y. Kawata, N. Niki, H. Ohmatsu, and N. Moriyama, “An algorithm for the extraction of pulmonary fissures from low-dose multi slice CT image,” Syst. Comput. Japan, Vol. 37, pp. 63–76, 2006. doi: 10.1002/scj.20167
   Google Scholar
• M. Prabukumar, L. Agilandeeswari, and K. Ganesan, “An intelligent lung cancer diagnosis system using cuckoo search optimization and support vector machine classifier,” J. Ambient. Intell. Humaniz. Comput. 2017.
   Web of Science ®Google Scholar
• M. M. Theresa, and V. S. Bharathi, “Computer aided diagnostic (CAD) for feature extraction of lungs in chest radiograph using different transform features,” Biomed. Res., 1–8, 2017.
   PubMed Web of Science ®Google Scholar
• W. Shen, M. Zhou, F. Yang, D. Yu, D. Dong, C. Yang, Y. Zang, and J. Tian, “Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification,” Pattern Recognit., Vol. 61, pp. 663–673, 2017. doi: 10.1016/j.patcog.2016.05.029
   Web of Science ®Google Scholar
• K. Liu, and G. Kang, “Multiview convolutional neural networks for lung nodule classification,” Int. J. Imaging Syst. Technol., Vol. 27, pp. 12–22, 2017. doi: 10.1002/ima.22206
   Web of Science ®Google Scholar
• S. Mirjalili, and A. Lewis, “The whale optimization algorithm,” Adv. Eng. Softw., Vol. 95, pp. 51–67, 2016. doi: 10.1016/j.advengsoft.2016.01.008
   Web of Science ®Google Scholar
• S. Cui, Z. Cheng, W. Qin, and L. Jiang, “Exosomes as a liquid biopsy for lung cancer,” Elsevier, pp. 46–51, 2018.
   Google Scholar