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A Review of most Recent Lung Cancer Detection Techniques using Machine Learning Dakhaz Mustafa Abdullah & Nawzat Sadiq Ahmed

A Review of most Recent Lung Cancer Detection Techniques using Machine Learning

Author (s)

Dakhaz Mustafa Abdullah & Nawzat Sadiq Ahmed

Abstract

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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.

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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
Website: ijsab.com
ISSN: ISSN 2520-4750 (Online), ISSN 2521-3040 (Print)
DOI: https://doi.org/10.5281/zenodo.4536818
Media: Online
Volume: 5
Issue: 3
Acceptance Date: 06/02/2021
Date of Publication: 12/02/2021
PDF URL: https://ijsab.com/wp-content/uploads/695.pdf
Free download: Available
Page: 159-173
First Page: 159
Last Page: 173
Paper Type: Literature Review
Current Status: Published

 

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

Retrieved from https://ijsab.com/wp-content/uploads/695.pdf

 

About Author (s)

Dakhaz Mustafa Abdullah (corresponding author), Information Technology, Technical College of Informatics, Akre Information Technology Management, Duhok Polytechnic University, Iraq. Email: dakhaz.abdullah@dpu.edu.kud

Nawzat Sadiq Ahmed, Information Technology Management, Technical College of Administration, DPU, Iraq.  Email: nawzat.ahmed@dpu.edu.krd 

 

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DOI: https://doi.org/10.5281/zenodo.4536818

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Semantic Search Engine Optimisation (SSEO) for Dynamic Websites: A Review Mohammed J. Sadeeq & Subhi R. M. Zeebaree

Semantic Search Engine Optimisation (SSEO) for Dynamic Websites: A Review

Author (s)

Mohammed J. Sadeeq & Subhi R. M. Zeebaree

Abstract

­The billions of databases, worldwide, provide an increasing amount of information to the people. As a result, the researchers have to seek knowledge about the resources, which were generically known as the search engines. One such search technique that is popularly used is the semantic search technique which improves the search accuracy by determining the purpose of the search along with the contextual meaning of the terms which appeared in the data space or the web for generating accurate results. Many search engines exist today, which makes it difficult to collect useful data. In this paper, many types of research depended which prepared to describe the Semantic Search and Semantic Web techniques. Various types of semantic search engines are investigated and the differences between the Semantic Search and Semantic Search keywords are determined. Additionally, the benefits of using Semantic Search were highlighted. The literature review and the findings of the case study helped in understanding the new constructs. These researches also determined the relationship between the new and the previously existing constructs based on their perspective regarding the extension of Bedny’s activity theory with regards to the SEO promotion techniques. They added the functional consequences of extending Bedny’s activity theory faced by the promotion managers. The researchers have summarised the history of semantic search and its global position in search engine generation. The researchers also highlighted the role played by the search engines in the semantic search and smart web technologies.

 Keywords: Semantic Search Engines, Semantic Web, Intelligent Search, Dynamic Websites.

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Title: Semantic Search Engine Optimisation (SSEO) for Dynamic Websites: A Review
Author: Mohammed J. Sadeeq & Subhi R. M. Zeebaree
Journal Name: International Journal of Science and Business
Website: ijsab.com
ISSN: ISSN 2520-4750 (Online), ISSN 2521-3040 (Print)
DOI: https://doi.org/10.5281/zenodo.4536804
Media: Online
Volume: 5
Issue: 3
Acceptance Date: 09/02/2021
Date of Publication: 12/02/2021
PDF URL: https://ijsab.com/wp-content/uploads/694.pdf
Free download: Available
Page: 148-158
First Page: 148
Last Page: 158
Paper Type: Literature Review
Current Status: Published

 

Cite This Article:

Sadeeq, M. J. & Zeebare, S. R. M. (2021). Semantic Search Engine Optimisation (SSEO) for Dynamic Websites: A Review.  International Journal of Science and Business, 5(3), 148-158. doi: https://doi.org/10.5281/zenodo.4536804

Retrieved from https://ijsab.com/wp-content/uploads/694.pdf

 

About Author (s)

Mohammed J. Sadeeq, Information Technology Department, Duhok Polytechnic University,  Duhok – Kurdistan Region, Iraq. Email: mohammed.jameel@uod.ac.

Subhi R. M. Zeebaree, (Corresponding author) Information Technology Department, Duhok Polytechnic University, Duhok – Kurdistan Region, Iraq. Emailil: subhi.rafeeq@dpu.edu.krd.

 

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DOI: https://doi.org/10.5281/zenodo.4536804