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Application of Deep Learning in the Diagnosis of Alzheimer's and Parkinson's disease-A Review.

Overview of attention for article published in Current Medical Imaging Formerly Current Medical Imaging Reviews, March 2023
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)

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2 Dimensions

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Title
Application of Deep Learning in the Diagnosis of Alzheimer's and Parkinson's disease-A Review.
Published in
Current Medical Imaging Formerly Current Medical Imaging Reviews, March 2023
DOI 10.2174/1573405620666230328113721
Pubmed ID
Authors

Asokan Suganya, Seshadri Lakshminarayanan Aarthy

Abstract

Most neurodegenerative diseases such as Alzheimer's and Parkinson's are life-threatening, critical, and incurable affecting mainly the elderly population. Early diagnosis is challenging as disease phenotype is very crucial for predicting, preventing the progression, and effective drug discovery. In the last few years, Deep learning (DL) based neural networks are the state-of-the-art models deployed in industries and academics across different areas like natural language processing, image analysis, speech recognition, audio classification, and many more. It has been slowly realized that they have a high potential in medical image analysis and diagnostics and medical management in general. As this field is vast and expanding rapidly, we have put focused on existing DL-based models to detect Alzheimer's and Parkinson's in particular. This study gives a summary of related medical examinations for these diseases. Frameworks and applications of many deep learning models have been discussed. We have given precise notes on pre-processing techniques used by various studies for MRI image analysis. An overview of the application of DL-based models in different stages of medical image analysis has been conferred. It has been realized from the review that more studies are focused on Alzheimer's compared to Parkinson's disease. Additionally, we have tabulated the various public datasets available for these diseases. We have highlighted the potential use of a novel biomarker for the early diagnosis of these disorders. Also, some challenges and issues in implementing deep learning techniques for the detection of these diseases have been addressed. Finally, we concluded with some directions for future research regarding deep learning in the diagnosis of these diseases.

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 1 7%
Unspecified 1 7%
Librarian 1 7%
Other 1 7%
Lecturer 1 7%
Other 2 13%
Unknown 8 53%
Readers by discipline Count As %
Computer Science 3 20%
Unspecified 1 7%
Chemical Engineering 1 7%
Environmental Science 1 7%
Social Sciences 1 7%
Other 0 0%
Unknown 8 53%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 April 2023.
All research outputs
#3,831,270
of 25,992,468 outputs
Outputs from Current Medical Imaging Formerly Current Medical Imaging Reviews
#1
of 1 outputs
Outputs of similar age
#72,573
of 426,959 outputs
Outputs of similar age from Current Medical Imaging Formerly Current Medical Imaging Reviews
#1
of 1 outputs
Altmetric has tracked 25,992,468 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. This one scored the same or higher as 0 of them.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 426,959 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them