Journal Information
Research Areas
Publication Ethics and Malpractice Statement
To Scholarlink Resource Center
Guidelines for Authors
For Authors
Instructions to Authors
Copyright forms
Submit Manuscript
Call for papers
Guidelines for Reviewers
For Reviewers
Review Forms
Contacts and Support
Support and Contact
List of Issues


Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS)


Article Title: Estimating An Optimal Backpropagation Algorithm for Training An ANN with the EGFR Exon 19 Nucleotide Sequence: An Electronic Diagnostic Basis for Non-Small Cell Lung Cancer(NSCLC)
by E. Adetiba, J. C. Ekeh, V. O. Matthews, S.A. Daramola, M.E.U. Eleanya

One of the most common forms of medical malpractices globally is an error in diagnosis. An improper diagnosis occurs when a doctor fails to identify a disease or report a disease when the patient is actually healthy. A disease that is commonly misdiagnosed is lung cancer. This cancer type is a major health problem internationally because it is responsible for 15% of all cancer diagnosis and 29% of all cancer deaths. The two major sub-types of lung cancer are; small cell lung cancer (about 13%) and non-small cell lung cancer (NSCLC- about 87%). The chance of surviving lung cancer depends on its correct diagnosis and/or the stage at the time it is diagnosed. However, recent studies have identified somatic mutations in the epidermal growth factor receptor (EGFR) gene in a subset of non-small cell lung cancer (NSCLC) tumors. These mutations occur in the tyrosine kinase domain of the gene. The most predominant of the mutations in all NSCLC patients examined is deletion mutation in exon 19 and it accounts for approximately 90% of the EGFR-activating mutations. This makes EGFR genomic sequence a good candidate for implementing an electronic diagnostic system for NSCLC. In this study aimed at estimating an optimum backpropagation training algorithm for a genomic based ANN system for NSCLC diagnosis, the nucleotide sequences of EGFR's exon 19 of a non-cancerous cell were used to train an artificial neural network (ANN). Several ANN back propagation training algorithms were tested in MATLAB R2008a to obtain an optimal algorithm for training the network. Of the nine different algorithms tested, we achieved the best performance (i.e. the least mean square error) with the minimum epoch (training iterations) and training time using the Levenberg-Marquardt algorithm.
Keywords: , NSCLC, EGFR, Lung Cancer, Diagnosis, Exon 19
Download full paper

ISSN: 2141-7016

Editor in Chief.

Prof. Gui Yun Tian
Professor of Sensor Technologies
School of Electrical, Electronic and Computer Engineering
University of Newcastle
United Kingdom



Copyright © Journal of Emerging Trends in Engineering and Applied Sciences 2010