University of Bahrain
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GAN-Based One-Class Classification SVM for Real time Medical Image Intrusion Detection

Show simple item record Hussain, Fadheela Tbarki, Khaoula Ksantini, Riadh 2023-03-16T09:11:26Z 2023-03-16T09:11:26Z 2023-03-16
dc.identifier.issn 2210-142X
dc.description.abstract Medical data attack and detection technology has been a hot topic in the past few decades precisely as numerous attacks on hospitals and clinics led to the loss of data. Although many methods have been developed for detection and discrimination of fake images, the problem has not yet been properly solved. Classifying the data method tends to produce higher error when compare to other methods due to the large variance directions. One-class classification is a fairly competitive method for detecting fake medical images due to the data's unbalanced nature. However, it can also produce higher error when compare to other methods. One of the most effective ways to improve the accuracy of one-class classification is by implementing covariance-guided support vector machine (iCOSVM) especially with a real time system. Therefore, in this paper, we present a case study that uses incremental covariance-guided support vector machine to build suitable detection system. The results of the study showed that the proposed detection system is very accurate and efficient. It utilizes the training data to improve its accuracy and minimize its error. The iCOSVM supports incremental projections, improves significantly the performance of the one-class support vector machine. Additionally, our proposed detection system is very accurate and efficient comparing to other incremental one class classifications algorithms, outperforming the batch learning system as well. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.subject Fake, Tumor detection; incremental learning; VGG-16, GAN, MRI scans, Medical images, synthesis images; one-class classification; multiclass classification, iCOSVM, iMOSVM, and iOSVM, detection application, supervised learning, DCNN, incremental Covariance-guided One-Class Support Vector Machine (iCOSVM). en_US
dc.title GAN-Based One-Class Classification SVM for Real time Medical Image Intrusion Detection en_US
dc.type Article en_US
dc.volume 13 en_US
dc.issue 1 en_US
dc.pagestart 625 en_US
dc.pageend 641 en_US
dc.contributor.authoraffiliation Department of Computer Science, College of Information Technology, Manama, Bahrain en_US
dc.contributor.authoraffiliation Artificial Intelligence at Private Higher School of Technology and Engineering, Tek-Up University, Tunisia en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US

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