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Please use this identifier to cite or link to this item:

http://hdl.handle.net/20.500.12358/27409
TitleBreast Cancer Severity Degree Predication Using Deep Learning Techniques
Title in Arabicتنبؤ مدى خطورة سرطان الثدي باستخدام تقنيات التعلم العميق
Abstract

Breast cancer is one of the most common types of cancer most often affecting women. It is a leading cause of cancer death in less developed countries. Thus, it is important to characterize the severity of the disease as soon as possible. In this paper, we applied deep learning methods to determine the severity degree of patients with breast cancer, using real data. The aim of this research is to characterize the severity of the disorder in a shorter time compared to the traditional methods. Deep learning methods are used because of their ability to detect target class more accurately than other machine learning methods, especially in the healthcare domain. In our research, several experiments were conducted using three different deep learning methods, which are: Deep Neural Network (DNN), Recurrent Neural Network (RNN) and Deep Boltzmann Machine (DBM). Then, we compared the performance of these methods with that of the traditional neural network method. We found that the f-measure of using the neural network was 74.52% compared to DNN which was 88.46 %, RNN which was 96.79% and DBM which was 97.28%.

Authors
El-Halees, Alaa
Tafish, Mohammed
TypeJournal Article
Date2020-03-01
LanguageEnglish
Subjects
Breast cancer severity
Medical data
Deep learning
Published inJordanian Journal of Computers and Information Technology
PublisherScopeMed Publishing
Citation
Item linkItem Link
DOI10.5455/jjcit.71-1568230142
ISSN24139351
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  • Staff Publications- Faculty of Information Technology [197]
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The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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