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Three Class Classification of Alzheimer’s Disease Using Deep Neural Networks

Overview of attention for article published in Current Medical Imaging Formerly Current Medical Imaging Reviews, January 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 (83rd percentile)

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8 Mendeley
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Title
Three Class Classification of Alzheimer’s Disease Using Deep Neural Networks
Published in
Current Medical Imaging Formerly Current Medical Imaging Reviews, January 2023
DOI 10.2174/1573405618666220929092341
Pubmed ID
Authors

Deep R. Shah, Rupal A. Kapdi, Jigna S. Patel, Jitali Patel

Abstract

Alzheimer's disease (AD) is prevalent dementia that can cause neurological brain disorders, poor decision making, impaired memory, mood swings, unstable emotions, and personality change. Deep neural networks are proficient in classifying Alzheimer's disease based on MRI images. This classification assists human experts in diagnosing AD and predicts its future progression. The paper proposes various Deep Neural Networks (DNN) for early AD detection to save cost and time for doctors, radiologists, and caregivers. A 3330-image-based Kaggle dataset is used to train the DNN, including 52 images of AD, 717 images of Mild Cognitive Impairment (MCI), and the remaining images of Cognitive Normal (CN). Stratified partitioning splits the dataset into 80% and 20% proportions for training and validation datasets. Proposed models include DenseNet169, DenseNet201, and ResNet152 DNNs with additional three fully-connected layers and softmax and Kullback Leibler Divergence (KLD) loss function. These models are trained considering pre-trained, partially pre-trained, and fully re-trained extended base models. The KLD loss function reduces the error and increases accuracy for all models. The partially pre-trained DenseNet201 model outperformed all the other models. DenseNet201 gives the highest accuracy of 99.98% for training, 99.07% for validation, and 95.66% for test datasets. The DenseNet201 model has the highest accuracy in comparison to other state-of-art-methods.

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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 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 38%
Student > Doctoral Student 1 13%
Researcher 1 13%
Unspecified 1 13%
Unknown 2 25%
Readers by discipline Count As %
Unspecified 1 13%
Nursing and Health Professions 1 13%
Sports and Recreations 1 13%
Neuroscience 1 13%
Engineering 1 13%
Other 0 0%
Unknown 3 38%
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 October 2022.
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
#77,423
of 482,636 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 482,636 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 83% 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