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Early detection of alzheimer{u2019}s disease using magnetic resonance imaging and diffusion tensor imaging / Eman Nabeel AbdAllah Marzban ; Supervised Ayman Mohamed Eldeib , Yasser Mostafa Kadah , Inas Ahmed Yassine

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Eman Nabeel AbdAllah Marzban , 2020Description: 125 P . : charts , facsmilies ; 30cmOther title:
  • الكشف المبكر لمرض آلزهايمر باستخدام صور الرنين المغناطيسى و صور مصفوفة الانتشار [Added title page title]
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Dissertation note: Thesis (Ph.D.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering Summary: Recently, classification and prediction of several diseases can be performed via machine learning methodologies. Of particular importance comes the neurodegenerative diseases, those related to losing neurons and brain cognitive functions, which encompasses Alzheimer{u2019}s Disease (AD). The large amount of data being readily-available and the increasing computer powers help boost the unleashed growing usage of these machine learning algorithms. The objectives of this work were 1) to find out the class activation maps (CAMs) deriving the network decision, and 2) to detect AD and its earlier pathology; namely, the mild cognitive impairment (MCI), from healthy controls (HC) in robust and low-cost network design. Both tasks were implemented using convolutional neural networks (CNNs)
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.03.Ph.D.2020.Em.E (Browse shelf(Opens below)) Not for loan 01010110082299000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.03.Ph.D.2020.Em.E (Browse shelf(Opens below)) 82299.CD Not for loan 01020110082299000

Thesis (Ph.D.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering

Recently, classification and prediction of several diseases can be performed via machine learning methodologies. Of particular importance comes the neurodegenerative diseases, those related to losing neurons and brain cognitive functions, which encompasses Alzheimer{u2019}s Disease (AD). The large amount of data being readily-available and the increasing computer powers help boost the unleashed growing usage of these machine learning algorithms. The objectives of this work were 1) to find out the class activation maps (CAMs) deriving the network decision, and 2) to detect AD and its earlier pathology; namely, the mild cognitive impairment (MCI), from healthy controls (HC) in robust and low-cost network design. Both tasks were implemented using convolutional neural networks (CNNs)

Issued also as CD

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