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Machine Learning-Based Multimodel Computing for Medical Imaging for Classification and Detection of Alzheimer Disease

Alzheimer is a disease that causes the brain to deteriorate over time. It starts off mild, but over the course of time, it becomes increasingly more severe. Alzheimer's disease causes damage to brain cells as well as the death of those cells. Memory in humans is especially susceptible to this....

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Autores principales: Alghamedy, Fatemah H., Shafiq, Muhammad, Liu, Lijuan, Yasin, Affan, Khan, Rehan Ali, Mohammed, Hussien Sobahi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9391119/
https://www.ncbi.nlm.nih.gov/pubmed/35990121
http://dx.doi.org/10.1155/2022/9211477
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author Alghamedy, Fatemah H.
Shafiq, Muhammad
Liu, Lijuan
Yasin, Affan
Khan, Rehan Ali
Mohammed, Hussien Sobahi
author_facet Alghamedy, Fatemah H.
Shafiq, Muhammad
Liu, Lijuan
Yasin, Affan
Khan, Rehan Ali
Mohammed, Hussien Sobahi
author_sort Alghamedy, Fatemah H.
collection PubMed
description Alzheimer is a disease that causes the brain to deteriorate over time. It starts off mild, but over the course of time, it becomes increasingly more severe. Alzheimer's disease causes damage to brain cells as well as the death of those cells. Memory in humans is especially susceptible to this. Memory loss is the first indication of Alzheimer's disease, but as the disease progresses and more brain cells die, additional symptoms arise. Medical image processing entails developing a visual portrayal of the inside of a body using a range of imaging technologies in order to discover and cure problems. This paper presents machine learning-based multimodel computing for medical imaging for classification and detection of Alzheimer disease. Images are acquired first. MRI images contain noise and contrast problem. Images are preprocessed using CLAHE algorithm. It improves image quality. CLAHE is better to other methods in its capacity to enhance the look of mammography in minute places. A white background makes the lesions more obvious to the naked eye. In spite of the fact that this method makes it simpler to differentiate between signal and noise, the images still include a significant amount of graininess. Images are segmented using the k-means algorithm. This results in the segmentation of images and identification of region of interest. Useful features are extracted using PCA algorithm. Finally, images are classified using machine learning algorithms.
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spelling pubmed-93911192022-08-20 Machine Learning-Based Multimodel Computing for Medical Imaging for Classification and Detection of Alzheimer Disease Alghamedy, Fatemah H. Shafiq, Muhammad Liu, Lijuan Yasin, Affan Khan, Rehan Ali Mohammed, Hussien Sobahi Comput Intell Neurosci Research Article Alzheimer is a disease that causes the brain to deteriorate over time. It starts off mild, but over the course of time, it becomes increasingly more severe. Alzheimer's disease causes damage to brain cells as well as the death of those cells. Memory in humans is especially susceptible to this. Memory loss is the first indication of Alzheimer's disease, but as the disease progresses and more brain cells die, additional symptoms arise. Medical image processing entails developing a visual portrayal of the inside of a body using a range of imaging technologies in order to discover and cure problems. This paper presents machine learning-based multimodel computing for medical imaging for classification and detection of Alzheimer disease. Images are acquired first. MRI images contain noise and contrast problem. Images are preprocessed using CLAHE algorithm. It improves image quality. CLAHE is better to other methods in its capacity to enhance the look of mammography in minute places. A white background makes the lesions more obvious to the naked eye. In spite of the fact that this method makes it simpler to differentiate between signal and noise, the images still include a significant amount of graininess. Images are segmented using the k-means algorithm. This results in the segmentation of images and identification of region of interest. Useful features are extracted using PCA algorithm. Finally, images are classified using machine learning algorithms. Hindawi 2022-08-12 /pmc/articles/PMC9391119/ /pubmed/35990121 http://dx.doi.org/10.1155/2022/9211477 Text en Copyright © 2022 Fatemah H. Alghamedy et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Alghamedy, Fatemah H.
Shafiq, Muhammad
Liu, Lijuan
Yasin, Affan
Khan, Rehan Ali
Mohammed, Hussien Sobahi
Machine Learning-Based Multimodel Computing for Medical Imaging for Classification and Detection of Alzheimer Disease
title Machine Learning-Based Multimodel Computing for Medical Imaging for Classification and Detection of Alzheimer Disease
title_full Machine Learning-Based Multimodel Computing for Medical Imaging for Classification and Detection of Alzheimer Disease
title_fullStr Machine Learning-Based Multimodel Computing for Medical Imaging for Classification and Detection of Alzheimer Disease
title_full_unstemmed Machine Learning-Based Multimodel Computing for Medical Imaging for Classification and Detection of Alzheimer Disease
title_short Machine Learning-Based Multimodel Computing for Medical Imaging for Classification and Detection of Alzheimer Disease
title_sort machine learning-based multimodel computing for medical imaging for classification and detection of alzheimer disease
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9391119/
https://www.ncbi.nlm.nih.gov/pubmed/35990121
http://dx.doi.org/10.1155/2022/9211477
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