LUNG CANCER DETECTION TECHNIQUE BASED ON SURF DESCRIPTOR AND KNN ALGORITHMS

Authors

  • Dr. Karim Q. Hussein Department of Computer Science, College of Science, Mustansiriya University, Baghdad, Iraq
  • Dalia Shihab Ahmed Department of Computer Science, College of Science, Mustansiriya University, Baghdad, Iraq

DOI:

https://doi.org/10.29121/granthaalayah.v9.i12.2021.4416

Keywords:

Lung Cancer, Detection Technique, SURF Descriptor, Feature Extraction, Machine Learning

Abstract [English]

In this century, lung cancer is undoubtedly one of the major serious health problems, and one of the leading causes of death for women and men worldwide. Despite advances in treating lung cancer with unprecedented products of pharmaceutical and technological advances, mortality and morbidity rates remain a major challenge for oncologists and cancer biologists. Thus, there is an urgent need to provide early, accurate, and effective diagnostic techniques to improve the survival rate and reduce morbidity and mortality related to lung cancer patients. Therefore, in this paper, an effective lung cancer screening technique is proposed for the early detection of risk factors for lung cancer. In this proposed technique, the powerful acceleration feature Speeded up robust feature (SURF) was used to extract the features. One of the machine learning methods was used to detect cancer by relying on the k nearest neighbor (KNN ) method, where the experimental results show an effective way to discover SURF features and tumor detection by relying on neighborhoods and calculating the distance using KNN. As a result, a high system sensitivity performance success rate of 96% and a system accuracy of 99% has been achieved.

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References

Ansari, Sadaf. (2019) "A review on SIFT and SURF for underwater image feature detection and matching." 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). Retrieved from https://doi.org/10.1109/ICECCT.2019.8869489 DOI: https://doi.org/10.1109/ICECCT.2019.8869489

Bergers, G. ; (2021) Fendt, S.M. The metabolism of cancer cells during metastasis. Nat. Rev. Cancer, 21, 162-180. Retrieved from https://doi.org/10.1038/s41568-020-00320-2 DOI: https://doi.org/10.1038/s41568-020-00320-2

Bermúdez, A. ; Arranz-Salas, I. ; Mercado, S. ; López-Villodres, J.A. ; González, V. ; Ríus, F. ; Ortega, M.V. ; Alba, C. ; Hierro, I. ; Bermúdez, D. (2021) Her2-Positive and Microsatellite Instability Status in Gastric Cancer-Clinicopathological Implications. Diagnostics, 11, 944. Retrieved from https://doi.org/10.3390/diagnostics11060944 DOI: https://doi.org/10.3390/diagnostics11060944

Bhatia S., Sinha Y., Goel L. (2019) Lung Cancer Detection: A Deep Learning Approach. In : Bansal J., Das K., Nagar A., Deep K., Ojha A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 817. Springer, Singapore. Retrieved from https://doi.org/10.1007/978-981-13-1595-4_55 DOI: https://doi.org/10.1007/978-981-13-1595-4_55

Dash, Puspita, and A. N. Sigappi. (2018) "Detection and Classification of Retinal Diseases in Spectral Domain Optical Coherence Tomography Images based on SURF descriptors." 2018 IEEE International Conference on System, Computation, Automation and Networking (ICSCA). IEEE, Retrieved from https://doi.org/10.1109/ICSCAN.2018.8541254 DOI: https://doi.org/10.1109/ICSCAN.2018.8541254

E. G. Fidalgo, (2011) "Experimental Assessment of Different Image Descriptors for Topological Map-Building and Scene Recognition," MSc Computer Science Thesis,

Gupta, Surbhi, Kutub Thakur, and Munish Kumar. (2021) “2D-human face recognition using SIFT and SURF descriptors of face's feature regions." The Visual Computer 37.3: 447-456. Retrieved from https://doi.org/10.1007/s00371-020-01814-8 DOI: https://doi.org/10.1007/s00371-020-01814-8

Huang X., Shan J., (2017) Vaidya V.Lung nodule detection in CT using 3D convolutional neural networks 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), IEEE, pp. 379-383. Retrieved from https://doi.org/10.1109/ISBI.2017.7950542 DOI: https://doi.org/10.1109/ISBI.2017.7950542

Joshya, Y. Camy, et al. (2021) "Automated Detection of Lung Cancer Based on Neuro Fuzzy Technique." Journal of Physics: Conference Series. Vol. 1979. No. 1. IOP Publishing, Retrieved from https://doi.org/10.1088/1742-6596/1979/1/012021 DOI: https://doi.org/10.1088/1742-6596/1979/1/012021

K. Roy et al., (2019) "A Comparative Study of Lung Cancer detection using supervised neural network," 2019 International Conference on Opto-Electronics and Applied Optics (Optronix), Kolkata, India,, pp. 1-5, 4 Retrieved from https://doi.org/10.1109/OPTRONIX.2019.8862326 DOI: https://doi.org/10.1109/OPTRONIX.2019.8862326

Kaur, Parneet, and Pratham Mittal. (2021) "A Comparative Study of LBPH, SIFT and SURF Algorithms for Face Recognition Task.".

Khorshid, Shler Farhad, and Adnan Mohsin Abdulazeez. (2021) "breast cancer diagnosis based on k-nearest neighbors : A review." PalArch's Journal of Archaeology of Egypt/Egyptology 18.4 : 1927-1951 Retrieved from https://archives.palarch.nl/index.php/jae/article/view/6601

Koo, M.M. ; Swann, R. ; McPhail, S. ; Abel, G.A. ; Elliss-Brookes, L. ; Rubin, G.P. ; Lyratzopoulos, G. (2020) Presenting symptoms of cancer and stage at diagnosis: Evidence from a cross-sectional, population-based study. Lancet Oncol., 21, 73-79. Retrieved from https://doi.org/10.1016/S1470-2045(19)30595-9 DOI: https://doi.org/10.1016/S1470-2045(19)30595-9

M. I. Faisal, S. Bashir, Z. S. Khan and F. Hassan Khan, (2018) "An Evaluation of Machine Learning Classifiers and Ensembles for Early Stage Prediction of Lung Cancer," 2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST), pp. 1-4, doi : 10.1109/ICEEST.2018.8643311. Retrieved from https://doi.org/10.1109/ICEEST.2018.8643311 DOI: https://doi.org/10.1109/ICEEST.2018.8643311

M. P. King, M. B. Anstey, and D. A. Vardy, (2013) "Comparison of Feature Detection Techniques for AUV Navigation Along a Trained Route," OCEANS, p. 8,

Nanda N., Kakkar P., (2019) Nagpal S.Computer-aided segmentation of liver lesions in CT scans using cascaded convolutional neural networks and genetically optimized classifier Arab. J. Sci. Eng., 44 (4), pp. 4049-4062. Retrieved from https://doi.org/10.1007/s13369-019-03735-8 DOI: https://doi.org/10.1007/s13369-019-03735-8

Oyallon, Edouard, and Julien Rabin. (2015) "An analysis of the SURF method." Image Processing On Line 5 : 176-218. Retrieved from https://doi.org/10.5201/ipol.2015.69 DOI: https://doi.org/10.5201/ipol.2015.69

Pankaj Nanglia, Sumit Kumar, Aparna N. Mahajan, Paramjit Singh, Davinder Rathee, (2020) A hybrid algorithm for lung cancer classification using SVM and Neural Networks,ICT Express, ,. Retrieved from https://doi.org/10.1016/j.icte.2020.06.007 DOI: https://doi.org/10.1016/j.icte.2020.06.007

Pinjala, Jahnavi, and Sujana Hanumara. (2021) "LUNG CANCER DETECTION USING SUPPORT VECTOR MACHINE." SPAST Abstracts 1.01. Retrieved from https://spast.org/techrep/article/view/758

Reddy U.J., Reddy B.R.V.R., (2019) Reddy B.E.Recognition of lung cancer using machine learning mechanisms with fuzzy neural networks Trait. Signal, 36 (1), pp. 87-91. Retrieved from https://doi.org/10.18280/ts.360111 DOI: https://doi.org/10.18280/ts.360111

Schüz, J. ; Espina, C. (2021) The eleventh hour to enforce rigorous primary cancer prevention. Mol. Oncol., 15, 741. Retrieved from https://doi.org/10.1002/1878-0261.12927 DOI: https://doi.org/10.1002/1878-0261.12927

Sharma, Manvinder, et al. (2020) "A novel approach of object detection using point feature matching technique for colored images." Proceedings of ICRIC 2019. Springer, Cham, 561-576. Retrieved from https://doi.org/10.1007/978-3-030-29407-6_40 DOI: https://doi.org/10.1007/978-3-030-29407-6_40

Sykora, Peter, Patrik Kamencay, and Robert Hudec. (2014) "Comparison of SIFT and SURF methods for use on hand gesture recognition based on depth map." Aasri Procedia 9 : 19-24. Retrieved from https://doi.org/10.1016/j.aasri.2014.09.005 DOI: https://doi.org/10.1016/j.aasri.2014.09.005

Vaishaw K, et al. (2018) An Innovative Approach for Investigation and Diagnosis of Lung Cancer by Utilizing Average Information Parameters, Elsevier Procedia of Computer Science, ;132 :525. Retrieved from https://doi.org/10.1016/j.procs.2018.05.005 DOI: https://doi.org/10.1016/j.procs.2018.05.005

Wang, Shudong, et al. (2020) "Classification of pathological types of lung cancer from CT images by deep residual neural networks with transfer learning strategy." Open Medicine 15.1: 190-197. Retrieved from https://doi.org/10.1515/med-2020-0028 DOI: https://doi.org/10.1515/med-2020-0028

Zhou, W. ; Liu, G. ; Hung, R.J. ; Haycock, P.C. ; Aldrich, M.C. ; Andrew, A.S. ; Arnold, S.M. ; Bickeböller, H. ; Bojesen, S.E. ; Brennan, P. ; et al. (2021) Causal relationships between body mass index, smoking and lung cancer : Univariable and multivariable Mendelian randomization. Int. J. Cancer, 148, 1077-1086. Retrieved from https://doi.org/10.1002/ijc.33292 DOI: https://doi.org/10.1002/ijc.33292

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Published

2021-12-30

How to Cite

Hussein, karim, & Ahmed, D. S. (2021). LUNG CANCER DETECTION TECHNIQUE BASED ON SURF DESCRIPTOR AND KNN ALGORITHMS. International Journal of Research -GRANTHAALAYAH, 9(12), 64–80. https://doi.org/10.29121/granthaalayah.v9.i12.2021.4416