Early Diagnose Alzheimer's Disease by Convolution Neural Network-based Histogram Features Extracting and Canny Edge
Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical ima...
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| Format: | Article |
| Language: | Arabic |
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College of Science for Women, University of Baghdad
2024-02-01
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| Series: | Baghdad Science Journal |
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| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9740 |
| _version_ | 1797292130887008256 |
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| author | Karrar A. Kadhim Farhan Mohamed Fallah H Najjar Ghalib Ahmed Salman |
| author_facet | Karrar A. Kadhim Farhan Mohamed Fallah H Najjar Ghalib Ahmed Salman |
| author_sort | Karrar A. Kadhim |
| collection | DOAJ |
| description |
Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop A Computer-Aided Brain Diagnosis (CABD) system that can tell if a brain scan exhibits indications of Alzheimer's disease. The system employs MRI and feature extraction methods to categorize images. This paper adopts the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset includes functional MRI and Positron-Version Tomography scans for Alzheimer's patient identification, which were produced for people with Alzheimer's as well as typical individuals. The proposed technique uses MRI brain scans to discover and categorize traits utilizing the Histogram Features Extraction (HFE) technique to be combined with the Canny edge to representing the input image of the Convolutional Neural Networks (CNN) classification. This strategy keeps track of their instances of gradient orientation in an image. The experimental result provided an accuracy of 97.7% for classifying ADNI images.
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| first_indexed | 2024-03-07T19:47:18Z |
| format | Article |
| id | doaj.art-5a125c18d06f4955847c30260f760694 |
| institution | Directory Open Access Journal |
| issn | 2078-8665 2411-7986 |
| language | Arabic |
| last_indexed | 2024-03-07T19:47:18Z |
| publishDate | 2024-02-01 |
| publisher | College of Science for Women, University of Baghdad |
| record_format | Article |
| series | Baghdad Science Journal |
| spelling | doaj.art-5a125c18d06f4955847c30260f7606942024-02-28T20:06:27ZaraCollege of Science for Women, University of BaghdadBaghdad Science Journal2078-86652411-79862024-02-01212(SI)10.21123/bsj.2024.9740Early Diagnose Alzheimer's Disease by Convolution Neural Network-based Histogram Features Extracting and Canny EdgeKarrar A. Kadhim0Farhan Mohamed1Fallah H Najjar2Ghalib Ahmed Salman3Department of Emerging Computing, Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia & Department of Computer Techniques Engineering, Faculty of Information Technology, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq.Department of Emerging Computing, Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia Department of Computer Systems Techniques, Technical Institute of Najaf, Al-Furat Al-Awsat Technical University, Najaf, Iraq.Department of Computer Science, Middle Technical University, Baghdad, Iraq. Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop A Computer-Aided Brain Diagnosis (CABD) system that can tell if a brain scan exhibits indications of Alzheimer's disease. The system employs MRI and feature extraction methods to categorize images. This paper adopts the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset includes functional MRI and Positron-Version Tomography scans for Alzheimer's patient identification, which were produced for people with Alzheimer's as well as typical individuals. The proposed technique uses MRI brain scans to discover and categorize traits utilizing the Histogram Features Extraction (HFE) technique to be combined with the Canny edge to representing the input image of the Convolutional Neural Networks (CNN) classification. This strategy keeps track of their instances of gradient orientation in an image. The experimental result provided an accuracy of 97.7% for classifying ADNI images. https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9740ADNI, Canny Edge, CNN, Early Diagnosis, Feature extraction, Histogram features, Precise Edge |
| spellingShingle | Karrar A. Kadhim Farhan Mohamed Fallah H Najjar Ghalib Ahmed Salman Early Diagnose Alzheimer's Disease by Convolution Neural Network-based Histogram Features Extracting and Canny Edge Baghdad Science Journal ADNI, Canny Edge, CNN, Early Diagnosis, Feature extraction, Histogram features, Precise Edge |
| title | Early Diagnose Alzheimer's Disease by Convolution Neural Network-based Histogram Features Extracting and Canny Edge |
| title_full | Early Diagnose Alzheimer's Disease by Convolution Neural Network-based Histogram Features Extracting and Canny Edge |
| title_fullStr | Early Diagnose Alzheimer's Disease by Convolution Neural Network-based Histogram Features Extracting and Canny Edge |
| title_full_unstemmed | Early Diagnose Alzheimer's Disease by Convolution Neural Network-based Histogram Features Extracting and Canny Edge |
| title_short | Early Diagnose Alzheimer's Disease by Convolution Neural Network-based Histogram Features Extracting and Canny Edge |
| title_sort | early diagnose alzheimer s disease by convolution neural network based histogram features extracting and canny edge |
| topic | ADNI, Canny Edge, CNN, Early Diagnosis, Feature extraction, Histogram features, Precise Edge |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9740 |
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