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Bladder Cancer Diagnosis using Artificial Neural Network

by Shaymaa M. Alkashef, Abdelhameed Ibrahim, Hesham Arafat, Tarek A. El-diasty
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 83 - Number 6
Year of Publication: 2013
Authors: Shaymaa M. Alkashef, Abdelhameed Ibrahim, Hesham Arafat, Tarek A. El-diasty
10.5120/14451-2709

Shaymaa M. Alkashef, Abdelhameed Ibrahim, Hesham Arafat, Tarek A. El-diasty . Bladder Cancer Diagnosis using Artificial Neural Network. International Journal of Computer Applications. 83, 6 ( December 2013), 11-17. DOI=10.5120/14451-2709

@article{ 10.5120/14451-2709,
author = { Shaymaa M. Alkashef, Abdelhameed Ibrahim, Hesham Arafat, Tarek A. El-diasty },
title = { Bladder Cancer Diagnosis using Artificial Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { December 2013 },
volume = { 83 },
number = { 6 },
month = { December },
year = { 2013 },
issn = { 0975-8887 },
pages = { 11-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume83/number6/14451-2709/ },
doi = { 10.5120/14451-2709 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:58:39.391013+05:30
%A Shaymaa M. Alkashef
%A Abdelhameed Ibrahim
%A Hesham Arafat
%A Tarek A. El-diasty
%T Bladder Cancer Diagnosis using Artificial Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 83
%N 6
%P 11-17
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The analysis of Magnetic Resonance Imaging (MRI) images using Artificial Neural Network (ANN)-based system is implemented in this paper to achieve a rapid and accurate diagnosis tool for bladder cancer. The proposed approach comprises image enhancement, removal of border, feature extraction and bladder cancer recognition using multilayer perception (MLP) with sequential weight/bias training function. We develop a model that defines the cancer level in order to enhance its treatment. Experimental results show that the devised approach increases the accuracy of diagnosis of bladder cancer up to 95%.

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Index Terms

Computer Science
Information Sciences

Keywords

Bladder cancer Image segmentation and ANN

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