{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T06:58:23Z","timestamp":1773817103620,"version":"3.50.1"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030158866","type":"print"},{"value":"9783030158873","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-15887-3_31","type":"book-chapter","created":{"date-parts":[[2019,7,19]],"date-time":"2019-07-19T14:02:57Z","timestamp":1563544977000},"page":"645-666","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Efficient Lung Image Classification Using GDA Based Feature Reduction and Tree Classifier"],"prefix":"10.1007","author":[{"given":"K.","family":"Vasanthi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"N. Bala","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,7,20]]},"reference":[{"key":"31_CR1","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, Y., Song, X., & Xiao, H. (2018). Adaptive multinomial regression with overlapping groups for multi-class classification of lung cancer. Computers in Biology and Medicine, 100, 1\u20139.","DOI":"10.1016\/j.compbiomed.2018.06.014"},{"key":"31_CR2","doi-asserted-by":"crossref","unstructured":"Kashyap, A., Gunjan, V. K., Kumar, A., Shaik, F., & Rao, A. A. (2016). Computational and Clinical Approach in Lung Cancer Detection and Analysis. Procedia Computer Science, 89, 528\u2013533.","DOI":"10.1016\/j.procs.2016.06.100"},{"key":"31_CR3","doi-asserted-by":"crossref","unstructured":"Wei, G., Ma, H., Qian, W., Han, F., Jiang, H., Qi, S., & Qiu, M. (2018). Lung nodule classification using local kernel regression models with out-of-sample extension. Biomedical Signal Processing and Control, 40, 1\u20139.","DOI":"10.1016\/j.bspc.2017.08.026"},{"key":"31_CR4","doi-asserted-by":"crossref","unstructured":"Lu, Z., Liu, Y., Xu, J., Yin, H., Yuan, H., Gu, J., \u2026 Xie, B. (2018). Immunohistochemical quantification of expression of a tight junction protein, claudin-7, in human lung cancer samples using digital image analysis method. Computer Methods and Programs in Biomedicine, 155, 179\u2013187.","DOI":"10.1016\/j.cmpb.2017.12.014"},{"key":"31_CR5","doi-asserted-by":"crossref","unstructured":"Song, Y., Cai, W., Huang, H., Zhou, Y., Wang, Y., & Feng, D. D. (2015). Locality-constrained Subcluster Representation Ensemble for lung image classification. Medical Image Analysis, 22(1), 102\u2013113.","DOI":"10.1016\/j.media.2015.03.003"},{"key":"31_CR6","doi-asserted-by":"crossref","unstructured":"Wang, Y., & Feng, L. (2018). Hybrid feature selection using component co-occurrence based feature relevance measurement. Expert Systems with Applications, 102, 83\u201399.","DOI":"10.1016\/j.eswa.2018.01.041"},{"key":"31_CR7","unstructured":"Bhuvaneswari, C., Aruna, P. and Loganathan, D., 2014. Classification of lung diseases by image processing techniques using computed tomography images. International Journal of Advanced Computer Research, 4(1), p. 87."},{"key":"31_CR8","doi-asserted-by":"crossref","unstructured":"Song, Q., Zhao, L., Luo, X. and Dou, X., 2017. Using deep learning for classification of lung nodules on computed tomography images. Journal of healthcare engineering, 2017.","DOI":"10.1155\/2017\/8314740"},{"key":"31_CR9","doi-asserted-by":"crossref","unstructured":"Dwivedi, S.A., Borse, R.P. and Yametkar, A.M., 2014. Lung Cancer detection and Classification by using Machine Learning & Multinomial Bayesian. IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), 9(1), pp. 69-75.","DOI":"10.9790\/2834-09136975"},{"key":"31_CR10","unstructured":"Keerthana, P., Thamilselvan, P. and Sathiaseelan, J.G.R., Detection of Lung Cancer in MR Images by using Enhanced Decision Tree Algorithm."},{"key":"31_CR11","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/j.talanta.2018.04.083","volume":"186","author":"E. Kaznowska","year":"2018","unstructured":"Kaznowska, E., Depciuch, J., \u0141ach, K., Ko\u0142odziej, M., Koziorowska, A., Vongsvivut, J., Cebulski, J. (2018). The classification of lung cancers and their degree of malignancy by FTIR, PCA-LDA analysis, and a physics-based computational model. Talanta, 186, 337\u2013345.","journal-title":"Talanta"},{"key":"31_CR12","doi-asserted-by":"crossref","unstructured":"Nagarajan, G., Minu, R. I., Muthukumar, B., Vedanarayanan, V., & Sundarsingh, S. D. (2016). Hybrid Genetic Algorithm for Medical Image Feature Extraction and Selection. Procedia Computer Science, 85, 455\u2013462.","DOI":"10.1016\/j.procs.2016.05.192"},{"key":"31_CR13","doi-asserted-by":"crossref","unstructured":"Ramos-Gonz\u00e1lez, J., L\u00f3pez-S\u00e1nchez, D., Castellanos-Garz\u00f3n, J. A., de Paz, J. F., & Corchado, J. M. (2017). A CBR framework with gradient boosting based feature selection for lung cancer subtype classification. Computers in Biology and Medicine, 86, 98\u2013106.","DOI":"10.1016\/j.compbiomed.2017.05.010"},{"key":"31_CR14","doi-asserted-by":"crossref","unstructured":"Azhar, R., Tuwohingide, D., Kamudi, D., Sarimuddin, & Suciati, N. (2015). Batik Image Classification Using SIFT Feature Extraction, Bag of Features and Support Vector Machine. Procedia Computer Science, 72, 24\u201330.","DOI":"10.1016\/j.procs.2015.12.101"},{"key":"31_CR15","unstructured":"Mohammed M., Al Samarraie, Md Jan Nordin, Ghassan Jasim Al-Anizy, 2015, Texture classification using random forests and support vector machines, Journal of Theoretical and Applied Information Technology, Vol. 73 No. 2, pp. 232-238."},{"key":"31_CR16","doi-asserted-by":"crossref","unstructured":"P. Thamilselvan and J. G. R. Sathiaseelan, 2016, Detection and Classification of Lung Cancer MRI Images by using Enhanced K Nearest Neighbor Algorithm, Journal of Science and Technology, Vol 9(43), pp. 1-7.","DOI":"10.17485\/ijst\/2016\/v9i43\/104642"},{"key":"31_CR17","doi-asserted-by":"crossref","unstructured":"Froz, B. R., de Carvalho Filho, A. O., Silva, A. C., de Paiva, A. C., Acatauass\u00fa Nunes, R., & Gattass, M. (2017). Lung nodule classification using artificial crawlers, directional texture and support vector machine. Expert Systems with Applications, 69, 176\u2013188.","DOI":"10.1016\/j.eswa.2016.10.039"},{"key":"31_CR18","unstructured":"Katuwal, R., Suganthan, P. N., & Zhang, L. (2017). An ensemble of decision trees with random vector functional link networks for multi-class classification. Applied Soft Computing."},{"key":"31_CR19","doi-asserted-by":"crossref","unstructured":"D\u00e9sir, C., Petitjean, C., Heutte, L., Thiberville, L., & Sala\u00fcn, M. (2012). An SVM-based distal lung image classification using texture descriptors. Computerized Medical Imaging and Graphics, 36(4), 264\u2013270.","DOI":"10.1016\/j.compmedimag.2011.11.001"},{"key":"31_CR20","doi-asserted-by":"crossref","unstructured":"Zia ur Rehman, M., Javaid, M., Shah, S. I. A., Gilani, S. O., Jamil, M., & Butt, S. I. (2018). An appraisal of nodules detection techniques for lung cancer in CT images. Biomedical Signal Processing and Control, 41, 140\u2013151.","DOI":"10.1016\/j.bspc.2017.11.017"},{"key":"31_CR21","doi-asserted-by":"crossref","unstructured":"Abdillah, B., Bustamam, A. and Sarwinda, D., 2017, October. Image processing based detection of lung cancer on CT scan images. In Journal of Physics: Conference Series (Vol. 893, No. 1, p. 012063). IOP Publishing.","DOI":"10.1088\/1742-6596\/893\/1\/012063"},{"key":"31_CR22","doi-asserted-by":"crossref","unstructured":"Kuruvilla, J., & Gunavathi, K. (2014). Lung cancer classification using neural networks for CT images. Computer Methods and Programs in Biomedicine, 113(1), 202\u2013209.","DOI":"10.1016\/j.cmpb.2013.10.011"},{"key":"31_CR23","doi-asserted-by":"crossref","unstructured":"Lee, S. L. A., Kouzani, A. Z., & Hu, E. J. (2010). Random forest based lung nodule classification aided by clustering. Computerized Medical Imaging and Graphics, 34(7), 535\u2013542.","DOI":"10.1016\/j.compmedimag.2010.03.006"},{"key":"31_CR24","doi-asserted-by":"crossref","unstructured":"Bhatnagar, D., Tiwari, A.K., Vijayarajan, V. and Krishnamoorthy, A., 2017, November. Classification of normal and abnormal images of lung cancer. In IOP Conference Series: Materials Science and Engineering (Vol. 263, No. 4, p. 042100). IOP Publishing.","DOI":"10.1088\/1757-899X\/263\/4\/042100"},{"key":"31_CR25","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.future.2018.10.009","volume":"92","author":"Lakshmanaprabu S.K.","year":"2019","unstructured":"Lakshmanaprabu S. K, Sachi Nandan Mohanty, K. Shankar, Arunkumar N, Gustavo Ramireze, Optimal deep learning model for classification of lung cancer on CT images, Future Generation Computer Systems, October 2018. \n                  https:\/\/doi.org\/10.1016\/j.future.2018.10.009","journal-title":"Future Generation Computer Systems"},{"key":"31_CR26","doi-asserted-by":"publisher","unstructured":"K. Shankar, Mohamed Elhoseny, Lakshmanaprabu S K, Ilayaraja M, Vidhyavathi RM, Majid Alkhambashi. Optimal feature level fusion based ANFIS classifier for brain MRI image classification. Concurrency Computat Pract Exper. 2018;e4887. \n                  https:\/\/doi.org\/10.1002\/cpe.4887","DOI":"10.1002\/cpe.4887"},{"key":"31_CR27","doi-asserted-by":"crossref","unstructured":"Lakshmanaprabu, S. K., Shankar, K., Khanna, A., Gupta, D., Rodrigues, J. J., Pinheiro, P. R., & De Albuquerque, V. H. C. (2018). Effective Features to Classify Big Data Using Social Internet of Things. IEEE Access, 6, 24196-24204.","DOI":"10.1109\/ACCESS.2018.2830651"},{"key":"31_CR28","doi-asserted-by":"publisher","unstructured":"Shankar, K., Lakshmanaprabu, S. K., Gupta, D., Maseleno, A., & de Albuquerque, V. H. C. (2018). Optimal feature-based multi-kernel SVM approach for thyroid disease classification. The Journal of Supercomputing, 2018. \n                  https:\/\/doi.org\/10.1007\/s11227-018-2469-4","DOI":"10.1007\/s11227-018-2469-4"},{"key":"31_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2018\/3569351","volume":"2018","author":"S. K. Lakshmanaprabu","year":"2018","unstructured":"Lakshmanaprabu SK, K. Shankar, Deepak Gupta, Ashish Khanna, Joel J. P. C. Rodrigues, Pl\u00e1cido R. Pinheiro, Victor Hugo C. de Albuquerque, \u201cRanking Analysis for Online Customer Reviews of Products Using Opinion Mining with Clustering,\u201d Complexity, vol. 2018, Article ID 3569351, 9 pages, 2018. \n                  https:\/\/doi.org\/10.1155\/2018\/3569351\n                  \n                .","journal-title":"Complexity"},{"key":"31_CR30","doi-asserted-by":"publisher","unstructured":"T. Avudaiappan, R. Balasubramanian, S. Sundara Pandiyan, M. Saravanan, S. K. Lakshmanaprabu, K. Shankar, Medical Image Security Using Dual Encryption with Oppositional Based Optimization Algorithm, Journal of Medical Systems, Volume 42, Issue 11, pp. 1-11, November 2018. \n                  https:\/\/doi.org\/10.1007\/s10916-018-1053-z","DOI":"10.1007\/s10916-018-1053-z"},{"key":"31_CR31","doi-asserted-by":"crossref","unstructured":"Elhoseny, M., Ram\u00edrez-Gonz\u00e1lez, G., Abu-Elnasr, O. M., Shawkat, S. A., Arunkumar, N., & Farouk, A. (2018). Secure medical data transmission model for IoT-based healthcare systems. IEEE Access, 6, 20596\u201320608.","DOI":"10.1109\/ACCESS.2018.2817615"},{"key":"31_CR32","doi-asserted-by":"crossref","unstructured":"Shehab, A., Elhoseny, M., Muhammad, K., Sangaiah, A. K., Yang, P., Huang, H., & Hou, G. (2018). Secure and robust fragile watermarking scheme for medical images. IEEE Access, 6, 10269-10278.","DOI":"10.1109\/ACCESS.2018.2799240"},{"key":"31_CR33","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.optlastec.2018.06.061","volume":"110","author":"Sonali","year":"2019","unstructured":"Sonali, Sima Sahu, Amit Kumar Singh, S.P. Ghrera, Mohamed Elhoseny, An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE, Optics & Laser Technology, Available online 5 July 2018 (DOI: \n                  https:\/\/doi.org\/10.1016\/j.optlastec.2018.06.061\n                  \n                )","journal-title":"Optics & Laser Technology"}],"container-title":["Handbook of Multimedia Information Security: Techniques and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-15887-3_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,7,19]],"date-time":"2019-07-19T14:53:34Z","timestamp":1563548014000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-15887-3_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030158866","9783030158873"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-15887-3_31","relation":{},"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"20 July 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}