Language:

Accumulated grey-level image representation for classification of lung cancer genetic mutations employing 2D principle component analysis

Abdelrahman, S.A ; Abdelwahab, M.M

Electronics letters, 2018-02, Vol.54 (4), p.194-196 [Peer Reviewed Journal]

Full text available

Citations Cited by

Actions
1. Add to e-Shelf
2. Remove from e-Shelf
3. E-mail
4. Print
5. Permalink
6. Citation
7. EndNote
8. Export RIS

Title:
Accumulated grey-level image representation for classification of lung cancer genetic mutations employing 2D principle component analysis
Author: Abdelrahman, S.A ; Abdelwahab, M.M
Subjects: 2D principle component analysis ; accumulated grey‐level image method ; accumulated grey‐level image representation ; AGLI method ; Biomedical technology ; cancer ; gene mutations ; gene representation ; gene sequence ; image domain ; image representation ; insidious disease ; low‐dimensional algorithm ; lung ; lung cancer genetic mutations ; medical image processing ; patient treatment ; principal component analysis ; treatment decisions
Is Part Of: Electronics letters, 2018-02, Vol.54 (4), p.194-196
Description: Lung cancer is an insidious disease, producing no symptoms until the disease spreads widely in the human body. Mutations of genes are the first alarm of such a disease in the human body. Therefore, classifying these mutations could provide guidance for the treatment decisions for lung cancer. In this Letter, a novel accumulated grey-level image (AGLI) method for gene representation is introduced, where each base in gene sequence is represented by accumulated number based on its order in gene sequence and then reflected into image domain. AGLI is incorporated with 2D principle component analysis to build accurate and low-dimensional algorithm for classifying the genetic mutations. Proposed algorithm was applied on the top 10 effective genes in lung cancer, where an accuracy of 99.27% was achieved. Experimental results show that the proposed algorithm enhanced the accuracy of classification and reduced the classification time for mutation in lung cancer relative to the existing methods.
Publisher: The Institution of Engineering and Technology
Language: English
Identifier: ISSN: 0013-5194
ISSN: 1350-911X
EISSN: 1350-911X
DOI: 10.1049/el.2017.1890
Source: Wiley Online Library Open Access

Links

View record in Wiley Blackwell$$FView record in $$GWiley Blackwell

Back to results list

Result 1 Next Go to Next page

Accumulated grey-level image representation for classification of lung cancer genetic mutations employing 2D principle component analysis

Abdelrahman, S.A ; Abdelwahab, M.M

Searching Remote Databases, Please Wait