BLADDER CANCER RECOGNITION: NEW TREND
AlKashef, S., Ibrahim, A., Arafat, H., El-Diasty, T. (2015). BLADDER CANCER RECOGNITION: NEW TREND. EKB Journal Management System, 15(2), 83-96. doi: 10.21608/ijicis.2015.15758
S AlKashef; A Ibrahim; H Arafat; T El-Diasty. "BLADDER CANCER RECOGNITION: NEW TREND". EKB Journal Management System, 15, 2, 2015, 83-96. doi: 10.21608/ijicis.2015.15758
AlKashef, S., Ibrahim, A., Arafat, H., El-Diasty, T. (2015). 'BLADDER CANCER RECOGNITION: NEW TREND', EKB Journal Management System, 15(2), pp. 83-96. doi: 10.21608/ijicis.2015.15758
AlKashef, S., Ibrahim, A., Arafat, H., El-Diasty, T. BLADDER CANCER RECOGNITION: NEW TREND. EKB Journal Management System, 2015; 15(2): 83-96. doi: 10.21608/ijicis.2015.15758

BLADDER CANCER RECOGNITION: NEW TREND

International Journal of Intelligent Computing and Information Sciences
Article 6, Volume 15, Issue 2, April 2015, Page 83-96  XML
Document Type: Original Article
DOI: 10.21608/ijicis.2015.15758
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Authors
S AlKashef1; A Ibrahim1; H Arafat1; T El-Diasty2
1Computer Engineering and Systems Department , Faculty of Engineering, Mansoura University - Egypt
2Urology & Nephrology Center, Mansoura University - Egypt
Abstract
Magnetic Resonance Imaging (MRI) has been widely applied to various medical procedures. The daily growth of medical data leads to human mistakes in manual analysis; and increases the need for automatic analysis.  Therefore, applying tools to collect, classify, and analyse medical data automatically is essential. Medical imaging issues are extremely complex, due to the high importance of correct diagnosis and treatment of diseases in healthcare systems. For these reasons, automatic medical image analysis algorithms are used to help increase the reliability and accurate understanding of medical images. The objective of this paper is to investigate the use of Artificial Neural Network (ANN) algorithms, such as multilayer perceptron (MLP), Jordan/Eleman network, Self-Organizing Feature Map (SOFM) and Support Vector Machine (SVM), to early detect bladder cancer (diagnosis), to determine tumour staging (for the sake of prognosis), and to assess the accuracy of MRI in T staging bladder cancer. A set of functional images, taken by Magnetic Resonance (MR), was used. It was found that multilayer perceptron (MLP) neural network gave better results than all other algorithms. We developed a model that defines cancer level in order to enhance its treatment. Experimental results show that the devised approach increases the accuracy of bladder cancer diagnosis to 81.8% using Generalized Feed Forward (GFF) after processing for more than 40 hours.
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