Authors :
Aafreen
Volume/Issue :
Volume 7 - 2022, Issue 7 - July
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3vJbr4R
DOI :
https://doi.org/10.5281/zenodo.6965357
Abstract :
A neurological condition called Alzheimer's
disease causes the death of brain cells. Dementia, which is
characterised by a loss of analytical skills and the ability
to carry out daily duties independently, is most frequently
caused by this. People of all ages are susceptible to the
dementia known as Alzheimer's disease (AD). Recently,
these indicators have been quickly incorporated into the
signs and symptoms of Alzheimer's disease (AD) using
classification frameworks that provide diagnostic tools.
This study conducts a thorough review of published
studies on Alzheimer's disease with a focus on computeraided diagnosis techniques such as magnetic resonance
imaging (MRI), computerised tomography (CT) scans,
imaging with diffusion tensors, and PET scans (positron
emission tomography). This article reviews some of the
most recent research on Alzheimer's disease and discusses
how machine learning (ML), deep learning (DL), and
other brain imaging techniques can help with an earlier
identification of theAt the conclusion of this research, a
CNN model that incorporates Densenet 169, EfficientNet,
and VGG-16 has been created to identify Alzheimer's
disease using Magnetic Resonance Imaging (MRI) data.
The Kaggle Alzheimer's dataset is used in experiments,
and the results demonstrate that the suggested models
had excellent accuracy
Keywords :
Neurogenerative illness, Dementia, Alzhiemer's detection, Deep Learning, and Machine Learning.
A neurological condition called Alzheimer's
disease causes the death of brain cells. Dementia, which is
characterised by a loss of analytical skills and the ability
to carry out daily duties independently, is most frequently
caused by this. People of all ages are susceptible to the
dementia known as Alzheimer's disease (AD). Recently,
these indicators have been quickly incorporated into the
signs and symptoms of Alzheimer's disease (AD) using
classification frameworks that provide diagnostic tools.
This study conducts a thorough review of published
studies on Alzheimer's disease with a focus on computeraided diagnosis techniques such as magnetic resonance
imaging (MRI), computerised tomography (CT) scans,
imaging with diffusion tensors, and PET scans (positron
emission tomography). This article reviews some of the
most recent research on Alzheimer's disease and discusses
how machine learning (ML), deep learning (DL), and
other brain imaging techniques can help with an earlier
identification of theAt the conclusion of this research, a
CNN model that incorporates Densenet 169, EfficientNet,
and VGG-16 has been created to identify Alzheimer's
disease using Magnetic Resonance Imaging (MRI) data.
The Kaggle Alzheimer's dataset is used in experiments,
and the results demonstrate that the suggested models
had excellent accuracy
Keywords :
Neurogenerative illness, Dementia, Alzhiemer's detection, Deep Learning, and Machine Learning.