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Abstract

Alzheimer's disease (AD) is a type of dementia that affects people as they get older and is one of the most frequent memories 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 the 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 are 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 techniques are applied to Magnetic Resonance Imaging (MRI) images gathered from the ADNI dataset. The performance of the proposed approach is analyzed based on Recall, precision, and accuracy metrics. The investigation results show that the proposed technique has 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 and forecast neurodegenerative brain illnesses like AD.

Keywords

Deep learning Fractalnet, Xception Unet Alzheimer’s disease Magnetic Resonance (MRI)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Adaobi, C. C. ., A, A. M. ., Asaarik, M. J. A., Miezah, N. A. ., & Odum, J. K. . (2022). A Hybrid Multi-Class Classification of Alzheimer Disease Based on Operative Deep Learning Techniques: Xception-Fractalnet : A Hybrid Multi-Class Classification of Alzheimer Disease Based on Operative Deep Learning Techniques. Convergence Chronicles, 4(1), 796–812. https://doi.org/10.53075/Ijmsirq/56646656
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