Detection of Cancer using Machine Learning Algorithms

Authors

  • Shelar Akshay Dilip Student, Department of Computer Engineering, Alard College of Engineering and Management, Pune, Maharashtra, India

DOI:

https://doi.org/10.37591/tmd.v7i3.4956

Keywords:

Cancer, artificial intelligence, neural network, image processing, prediction, analysis, oncology, radiology, decision tree algorithm, fuzzy c-mean clustering algorithm, early detection

Abstract

Cancer is a group of diseases characterized by uncontrolled growth and spread of abnormal cells. There are over 100 types of cancer. And any part of the body can be affected. Cancer has become 2nd leading cause of death. Some hospitals offer cancer screening tests; the test results need to be evaluated by an oncologist. The cancer screening test are very expensive and not available in all of the hospital. The expensive nature of test causes economically weaker section to skip the routine check-ups, most privilege class people tends to skip the routine check-ups too due to scarcity of check-up facilities in their cities, villagers too are affected by these two causes. Early detection of cancer can help in eradicating the disease from patient, but if it is left unchecked and is discovered at critical stage, treatment of the disease is very difficult. Using advanced algorithms and harnessing full potential of machine learning, this screening test can be availed at all the hospitals and clinics and the system could predict the disease with a precision such that making oncologist’s intervention unneeded in most cases.

References

Punithavathy K, Ramya MM, Poobal S. Analysis of statistical texture features for automatic lung cancer detection in PET/CT images. In2015 International Conference on Robotics, Automation, Control and Embedded Systems (RACE) 2015 Feb 18, pp. 1–5.

Miah MB, Yousuf MA. Detection of lung cancer from CT image using image processing and neural network. In2015 International conference on electrical engineering and information

communication technology (ICEEICT) 2015 May 21 pp. 1–6.

Chaudhary A, Singh SS. Lung cancer detection on CT images by using image processing. In2012 International Conference on Computing Sciences 2012 Sep 14, pp. 142–146.

Sundar RS, Vadivel M. Performance analysis of melanoma early detection using skin lesion classification system. In2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT) 2016 Mar 18 pp. 1–5.

Alfed N, Khelifi F, Bouridane A. Improving a bag of words approach for skin cancer detection in dermoscopic images. In2016 International Conference on Control, Decision and Information Technologies (CoDIT) 2016 Apr 6, pp. 024–027.

Mhaske HR, Phalke DA. Melanoma skin cancer detection and classification based on supervised and unsupervised learning. In 2013 International conference on Circuits, Controls and Communications (CCUBE) 2013 Dec 27, pp. 1–5.

Wahba MA, Ashour AS, Napoleon SA, Abd Elnaby MM, Guo Y. Combined empirical mode decomposition and texture features for skin lesion classification using quadratic support vector machine. Health Information Science and Systems. 2017 Dec 1;5(1):10.

Uzma Jamil, Shehzad Khalid. Medical Imaging. 2017; 1327.

X. Hu and H. Zhao. Selected image processing examples using Visual C++ and Matlab. Posts & Telecom Press. Beijing. 2004;187–195.

Jia X, Lu Y. Fuzzy information processing. National University of Defence Technology Press, Changsha. 1996.

Cai K, Liu M, Zhang Y. The automatic detection algorithm of tongue cancer stem cells based on fuzzy pattern recognition. In 2011 6th International Conference on Computer Science & Education (ICCSE) 2011 Aug 3, pp. 240–244.

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Published

2021-02-01

Issue

Vol. 7 No. 3 (2020)

Section

Articles

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