Diagnosis and Detection of Alzheimer’s Disease Using Learning Algorithm
In Computer-Aided Detection (CAD) brain disease classification is a vital issue. Alzheimer’s Disease (AD) and brain tumors are the primary reasons of death. The studies of these diseases are carried out by Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2023-12-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2022.9020049 |
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| author | Gargi Pant Shukla Santosh Kumar Saroj Kumar Pandey Rohit Agarwal Neeraj Varshney Ankit Kumar |
| author_facet | Gargi Pant Shukla Santosh Kumar Saroj Kumar Pandey Rohit Agarwal Neeraj Varshney Ankit Kumar |
| author_sort | Gargi Pant Shukla |
| collection | DOAJ |
| description | In Computer-Aided Detection (CAD) brain disease classification is a vital issue. Alzheimer’s Disease (AD) and brain tumors are the primary reasons of death. The studies of these diseases are carried out by Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography (CT) scans which require expertise to understand the modality. The disease is the most prevalent in the elderly and can be fatal in its later stages. The result can be determined by calculating the mini-mental state exam score, following which the MRI scan of the brain is successful. Apart from that, various classification algorithms, such as machine learning and deep learning, are useful for diagnosing MRI scans. However, they do have some limitations in terms of accuracy. This paper proposes some insightful pre-processing methods that significantly improve the classification performance of these MRI images. Additionally, it reduced the time it took to train the model of various pre-existing learning algorithms. A dataset was obtained from Alzheimer’s Disease Neurological Initiative (ADNI) and converted from a 4D format to a 2D format. Selective clipping, grayscale image conversion, and histogram equalization techniques were used to pre-process the images. After pre-processing, we proposed three learning algorithms for AD classification, that is random forest, XGBoost, and Convolution Neural Networks (CNN). Results are computed on dataset and show that it outperformed with exiting work in terms of accuracy is 97.57% and sensitivity is 97.60%. |
| format | Article |
| id | doaj-art-11b0621f8dd34b42b12615a6e3552921 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-11b0621f8dd34b42b12615a6e35529212025-02-03T02:57:52ZengTsinghua University PressBig Data Mining and Analytics2096-06542023-12-016450451210.26599/BDMA.2022.9020049Diagnosis and Detection of Alzheimer’s Disease Using Learning AlgorithmGargi Pant Shukla0Santosh Kumar1Saroj Kumar Pandey2Rohit Agarwal3Neeraj Varshney4Ankit Kumar5Department of Management, Doon Business School, Dehradun 248001, IndiaJaipuria Institute of Management, Jaipur 302033, IndiaDepartment of Computer Engineering and Apllications, GLA University, Mathura 281406, IndiaDepartment of Computer Engineering and Apllications, GLA University, Mathura 281406, IndiaDepartment of Computer Engineering and Apllications, GLA University, Mathura 281406, IndiaDepartment of Computer Engineering and Apllications, GLA University, Mathura 281406, IndiaIn Computer-Aided Detection (CAD) brain disease classification is a vital issue. Alzheimer’s Disease (AD) and brain tumors are the primary reasons of death. The studies of these diseases are carried out by Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography (CT) scans which require expertise to understand the modality. The disease is the most prevalent in the elderly and can be fatal in its later stages. The result can be determined by calculating the mini-mental state exam score, following which the MRI scan of the brain is successful. Apart from that, various classification algorithms, such as machine learning and deep learning, are useful for diagnosing MRI scans. However, they do have some limitations in terms of accuracy. This paper proposes some insightful pre-processing methods that significantly improve the classification performance of these MRI images. Additionally, it reduced the time it took to train the model of various pre-existing learning algorithms. A dataset was obtained from Alzheimer’s Disease Neurological Initiative (ADNI) and converted from a 4D format to a 2D format. Selective clipping, grayscale image conversion, and histogram equalization techniques were used to pre-process the images. After pre-processing, we proposed three learning algorithms for AD classification, that is random forest, XGBoost, and Convolution Neural Networks (CNN). Results are computed on dataset and show that it outperformed with exiting work in terms of accuracy is 97.57% and sensitivity is 97.60%.https://www.sciopen.com/article/10.26599/BDMA.2022.9020049alzheimer’s diseasedeep learningrandom forestxgboost |
| spellingShingle | Gargi Pant Shukla Santosh Kumar Saroj Kumar Pandey Rohit Agarwal Neeraj Varshney Ankit Kumar Diagnosis and Detection of Alzheimer’s Disease Using Learning Algorithm Big Data Mining and Analytics alzheimer’s disease deep learning random forest xgboost |
| title | Diagnosis and Detection of Alzheimer’s Disease Using Learning Algorithm |
| title_full | Diagnosis and Detection of Alzheimer’s Disease Using Learning Algorithm |
| title_fullStr | Diagnosis and Detection of Alzheimer’s Disease Using Learning Algorithm |
| title_full_unstemmed | Diagnosis and Detection of Alzheimer’s Disease Using Learning Algorithm |
| title_short | Diagnosis and Detection of Alzheimer’s Disease Using Learning Algorithm |
| title_sort | diagnosis and detection of alzheimer s disease using learning algorithm |
| topic | alzheimer’s disease deep learning random forest xgboost |
| url | https://www.sciopen.com/article/10.26599/BDMA.2022.9020049 |
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