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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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An Exploration: Alzheimer's Disease Classification Based on Convolutional Neural Network.

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  • 1Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.

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Convolutional neural networks (CNNs) show great promise for diagnosing Alzheimer's disease (AD) using neuroimaging data. Despite successes, challenges like limited medical imaging data require further research for improved early AD detection.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Alzheimer's disease (AD) is a leading neurodegenerative disorder causing cognitive decline.
  • Deep learning, particularly Convolutional Neural Networks (CNNs), offers advanced diagnostic capabilities for AD.
  • CNNs eliminate the need for manual feature extraction, unlike traditional machine learning algorithms.

Purpose of the Study:

  • To review current applications of CNNs in classifying Alzheimer's disease using neuroimaging data.
  • To analyze the effectiveness of various classification approaches, datasets, modalities, and preprocessing techniques for AD diagnosis.
  • To identify challenges and future scope for CNNs in Alzheimer's disease detection.

Main Methods:

  • Systematic literature search conducted in June 2021 across Google Scholar, IEEE Xplore, ACM Digital Library, and PubMed.
  • Focus on Convolutional Neural Network (CNN) applications for Alzheimer's disease classification.
  • Inclusion of studies utilizing single and multi-modality neuroimaging data.

Main Results:

  • CNNs have demonstrated significant success in classifying Alzheimer's disease.
  • The study examined diverse datasets, neuroimaging modalities, and preprocessing strategies.
  • Effectiveness of CNNs varies based on data characteristics and handling methods.

Conclusions:

  • CNNs are a powerful tool for Alzheimer's disease classification from neuroimaging data.
  • The scarcity of medical imaging data presents a significant challenge.
  • Further research is needed to overcome data limitations and enhance the scope of CNNs in early AD diagnosis.