SIS725430AI160223

16th February, 2023

A Hybrid Multi-Class Classification of Alzheimer Disease Based on OperativeDeep Learning Techniques: Xception-Fractalnet

Author(s):

Chukwuma Chinaza Adaobi, Atianashie Miracle A., Mathias Justice Akanzabwon Asaarik,Nyamike A Miezah,and James Kwabena Odum

Abstract:

Alzheimer's disease (AD) is a type of dementia that affects people as they get older and is one of the most frequent memory depletion diseases. Early-stage AD identification is essential for preventing and intervening the disease development. But it is a challenging task due to the complex structure of brain and its functions. Hence, the research on AD has increased recently. Therefore in this paper, an effective hybrid Xception and Fractalnet based deep learning framework is implemented to classify the stages of AD into five classes. To increase the performance of the classifier, effective pre-processing methods and Unet++ based segmentation technique are applied on Magnetic Resonance Imaging (MRI) images gathered from ADNI dataset. The performance of the proposed approach is analysed based on Recall, precision and accuracy metrics. The investigation results shows that the proposed technique have the capacity to attain 98.30% recall, 99.72% precision, and 99.06% accuracy in multiclass classification. The results indicate that the proposed techniques combined with MRI images can be utilized to categorize forecast neurodegenerative brain illnesses like AD.

Keywords:

Deep learning Fractalnet Xception Unet++ Alzheimer‟s disease Magnetic Resonance (MRI)

Scholars Journal of Science and Technology

Volume: 3

Issue: 4

Page numbers: 796-812

ISSN or DOI: 2955-7844

110 views

11 cite

Ghana

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