Lung cancer classification using data mining and supervised learning algorithms on multi-dimensional data set

Saadaldeen Rashid Ahmed Ahmed, Israa Al Barazanchi, Ammar Mhana, Haider Rasheed Abdulshaheed


These With recent developments in machine learning, data mining and computer vision, there is great potential for improvements in early detection of lung cancer using scans and data available. This paper details the methods and techniques used in our project, where the objective is to develop algorithms to determine whether a patient has or is likely to develop lung cancer using dataset images using data mining and machine learning for the classification and examination. We explore approaches to address the problem. Cancer is the most important cause of death globally. The disease diagnosis is a major process to treat the patients who are affected by cancer disease. The diagnosis process is more difficult comparatively known about the cancer disease detection. Developing a proposed data mining model is useful to diagnose the cancer disease once the cancer detection is accomplished using data mining for the examination and classification of machine learning supervised algorithms.


lung cancer detection, machine learning, data mining, classification.

Full Text:



RushilAnirudh, Jayaraman J Thiagarajan, Timo Bremer, and Hyojin Kim. Lung nodule detection using 3d classifier neural networks trained on weakly labeled data. In SPIE Medical Imaging, pages 978532-978532. International Society for Optics and Photonics, 2016.

Dan Ciresan, Alessandro Giusti, Luca M Gambardella, and J•urgenSchmidhuber. Deep neural networks segment neuronal membranes in electron microscopy images. In Advances in neural information processing systems, pages 2843-2851, 2012.

Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639):115-118, 2017.

Jacques Ferlay, Isabelle Soerjomataram, Rajesh Dikshit, Sultan Eser, Colin Math-ers, MariseRebelo, Donald Maxwell Parkin, David Forman, and Freddie Bray. Cancer incidence and mortality worldwide: sources, methods and major patterns in globocan 2012. International journal of cancer, 136(5):E359-E386, 2015.

Rotem Golan, Christian Jacob, and J•orgDenzinger. Lung nodule detection in images using deep classifier neural networks. In Neural Networks (IJCNN), 2016 International Joint Conference on, pages 243-250. IEEE, 2016.

Metin N Gurcan, BerkmanSahiner, Nicholas Petrick, Heang-Ping Chan, Ella A Kazerooni, Philip N Cascade, and LubomirHadjiiski. Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system. Medical Physics, 29(11):2552{2558, 2012}.

Devinder Kumar, Alexander Wong, and David A Clausi. Lung nodule classification using deep features in images. In Computer and Robot Vision (CRV), 2015 12th Conference on, pages 133-138. IEEE, 2015.

Fan Liao and Chunxia Zhao. Improved fuzzy c-means clustering algorithm for automatic detection of lung nodules. In Image and Signal Processing (CISP), 2015 8th International Congress on, pages 464-469. IEEE, 2015.

NegarMirderikvand, MarjanNaderan, and Amir Jamshidnezhad. Accurate automatic localisation of lung nodules using graph cut and snakes algorithms. In Computer and Knowledge Engineering (ICCKE), 2016 6th International Conference on, pages 194-199. IEEE, 2016.

Olaf Ronneberger, Philipp Fischer, and Thomas Brox. Classifier: convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 234-241. Springer, 2015.

Arnaud ArindraAdiyosoSetio, Alberto Traverso, Thomas de Bel, Moira SN Berens, Cas van den Bogaard, PiergiorgioCerello, Hao Chen, Qi Dou, Maria EvelinaFantacci, Bram Geurts, et al. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the luna16 challenge. arXiv preprint arXiv:1612.08012, 2016.

Sumit K Shah, Michael F McNitt-Gray, Sarah R Rogers, Jonathan G Goldin, Robert D Suh, James W Sayre, Iva Petkovska, Hyun J Kim, and Denise R Aberle. Computer-aided diagnosis of the solitary pulmonary nodule 1. Academic radiology, 12(5):570-575, 2015.

Wei Shen, Mu Zhou, Feng Yang, Caiyun Yang, and JieTian. Multi-scale convolutional neural networks for lung nodule classification. In International Conference on Information Processing in Medical Imaging, pages 588-599. Springer, 2016.

Akira Motohiro, Hitoshi Ueda, Hikotaro Komatsu, NoboruYanai, and Takashi Mori, “Prognosis of non-surgically treatedclinical stage I lung cancer patients in Japan,” Lung CancerJournal, vol.36, issue.1, pp.65-69, April 2016.

MariosAnthopoulos, StergiosChristodoulidis, Lukas Ebner,Andreas Christe and StavroulaMougiakakou, “Lung PatternClassification for Interstitial Lung Diseases Using a DeepConvolutional Neural Network,” IEEE Transaction on MedicalImaging, vol.35, May 2016.

J. AlameluMangai, JagadishNayak and V. Santhosh Kumar;“A Novel Approach for Classifying Medical Images Using DataMining Techniques,” International Journal of Computer Scienceand Electronics Engineering, vol.1, issue.2 2013.

PetrBerka, Jan Rauch and DjamelAbdelkaderZighed,“Ontologies in the Health Field,” in Data Mining and MedicalKnowledge management: Cases and Application, Hershey, UnitedStates: IGI Global, March 2011, pp. 37-56.

PetrBerka, Jan Rauch and DjamelAbdelkaderZighed, “Cost-Sensitive Learning in Medicine,” in Data Mining and MedicalKnowledge management: Cases and Application, Hershey, UnitedStates: IGI Global, March 2017, pp. 57–75.

PetrBerka, Jan Rauch and DjamelAbdelkaderZighed,“Classification and Prediction with Neural Networks,” in DataMining and Medical Knowledge management: Cases andApplication, Hershey, United States: IGI Global, March 2017, pp.76–107.

Thomas Erl, WajidKhattak, Paul Buhler, “Understanding BigData,” in Big Data Fundamentals, Crawfordsville, Indiana; USA:RR Donnelley, December 2015, pp. 21–35.

Cortes Corinna and Vapnik Vladimir, "Support-vectornetworks," Machine Learning, vol.20, issue.3, pp.273–297, September 2016.

Tsung-Yu Lin, AruniRoyChowdhury, SubhransuMaji,“Bilinear CNN Models for Fine-Grained Visual Recognition,” inIEEE International Conference on Computer Vision(ICCV),2015, pp. 1449-1457.

Abdulshaheed, H.R., Binti, S.A., and Sadiq, I.I., 2018. A Review on Smart Solutions Based-On Cloud Computing and Wireless Sensing. International Journal of Pure and Applied Mathematics, 119 (18), pp.461–486.

Shibghatullah, A.S. and Barazanchi, I. Al, 2014. An Analysis of the Requirements for Efficient Protocols in WBAN. Journal of Telecommunication, Electronic and Computer Engineering, 6 (2), pp.19–22.



  • There are currently no refbacks.

Copyright (c) 2019 Saadaldeen Rashid Ahmed Ahmed1, Israa Al_Barazanchi, Ammar mhana, Haider Rasheed Abdulshaheed

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License