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    •   BracU IR
    • School of Data and Sciences (SDS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    Alzheimer’s Disease detection and classification using transfer learning techniques and ensembling operations on convolutional neural networks

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    17101074, 17101061, 17101364, 17101397_CSE.pdf (1.877Mb)
    Date
    2021-01
    Publisher
    Brac University
    Author
    Awwal, Alvina
    Shomee, Homaira Huda
    Sadat, Sayed Us
    Amin, Sadia Nur
    Metadata
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    URI
    http://hdl.handle.net/10361/15161
    Abstract
    Alzheimer’s disease (AD) is a neurological disease that affects the healthy cells of the brain and results in people having long-term memory loss, thinking problems, disorientation, behavioral inconsistencies and finally death. When the disease gets detected, the pathological load is already high and there is no way of coming back from there. This neurodegenerative disease consists of three general stages which we classified in our research that includes very mild (early stage), mild (middle stage) and finally, the moderate stage (late stage). We implemented 5 existing efficient and recent CNN models such as VGG19, Inception-ResNet-v2, ResNet152v2, EfficientNetB5 and EfficientNetB6 including one model of our own. Later, we did ensembling operations thrice with multiple combinations of the models to enhance our outcome and that was achieved since this gave improved accuracy of up to around 96% compared to the individual models where the maximum was 92.2% from EfficientNetB5. The results achieved showed precise detection and classification of AD and its stages even though data was limited and it was a challenge differentiating a healthy brain from that of a subject with AD.
    Keywords
    Convolutional Neural Network (CNN); Alzheimer’s Disease (AD); Transfer Learning; Neural Network (NN); MRI; Deep Learning; Average Ensemble; VGG19; Inception-ResNet-v2; ResNet152v2; EfficientNetB5; EfficientNetB6
     
    Description
    This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
     
    Cataloged from PDF version of thesis.
     
    Includes bibliographical references (page 30-33).
    Department
    Department of Computer Science and Engineering, Brac University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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