A Review of most Recent Lung Cancer Detection Techniques using Machine Learning
Dakhaz Mustafa Abdullah & Nawzat Sadiq Ahmed
Lung cancer is a sort of dangerous cancer and difficult to detect. It usually causes death for both gender men & women therefore, so it is more necessary for care to immediately & correctly examine nodules. Accordingly, several techniques have been implemented to detect lung cancer in the early stages. In this paper a comparative analysis of different techniques based on machine learning for detection lung cancer have been presented. There have been too many methods developed in recent years to diagnose lung cancer, most of them utilizing CT scan images and some of them using x-ray images. In addition, multiple classifier methods are paired with numerous segmentation algorithms to use image recognition to identify lung cancer nodules. From this study it has been found that CT scan images are more suitable to have the accurate results. Therefore, mostly CT scan images are used for detection of cancer. Also, marker-controlled watershed segmentation provides more accurate results than other segmentation techniques. In Addition, the results that obtained from the methods based deep learning techniques achieved higher accuracy than the methods that have been implemented using classical machine learning techniques.
Keywords: Lung Cancer Detection, Machine Learning, Deep Learning, SCLC, and NSCLC.
|Title:||A Review of most Recent Lung Cancer Detection Techniques using Machine Learning|
|Author:||Dakhaz Mustafa Abdullah & Nawzat Sadiq Ahmed|
|Journal Name:||International Journal of Science and Business|
|ISSN:||ISSN 2520-4750 (Online), ISSN 2521-3040 (Print)|
|Date of Publication:||12/02/2021|
|Paper Type:||Literature Review|
Cite This Article:
Abdullah, D. M. & Ahmed, N. S. (2021). A Review of most Recent Lung Cancer Detection Techniques using Machine Learning. International Journal of Science and Business, 5(3), 159-173. doi: https://doi.org/10.5281/zenodo.4536818
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About Author (s)
Dakhaz Mustafa Abdullah (corresponding author), Information Technology, Technical College of Informatics, Akre Information Technology Management, Duhok Polytechnic University, Iraq. Email: firstname.lastname@example.org
Nawzat Sadiq Ahmed, Information Technology Management, Technical College of Administration, DPU, Iraq. Email: email@example.com