Diagnosis and Detection of Alzheimer's Disease Using Learning Algorithm Article Swipe
Gargi Pant Shukla
,
Santosh Kumar
,
Saroj Kumar Pandey
,
Rohit Agarwal
,
Neeraj Varshney
,
Ankit Kumar
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.26599/bdma.2022.9020049
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.26599/bdma.2022.9020049
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%.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.26599/bdma.2022.9020049
- https://ieeexplore.ieee.org/ielx7/8254253/10233239/10233244.pdf
- OA Status
- diamond
- Cited By
- 50
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386262510
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386262510Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.26599/bdma.2022.9020049Digital Object Identifier
- Title
-
Diagnosis and Detection of Alzheimer's Disease Using Learning AlgorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-29Full publication date if available
- Authors
-
Gargi Pant Shukla, Santosh Kumar, Saroj Kumar Pandey, Rohit Agarwal, Neeraj Varshney, Ankit KumarList of authors in order
- Landing page
-
https://doi.org/10.26599/bdma.2022.9020049Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/8254253/10233239/10233244.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/8254253/10233239/10233244.pdfDirect OA link when available
- Concepts
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Artificial intelligence, Computer science, Random forest, Machine learning, CAD, Computer-aided diagnosis, Convolutional neural network, Deep learning, Magnetic resonance imaging, Positron emission tomography, Pattern recognition (psychology), Radiology, Medicine, Engineering drawing, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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50Total citation count in OpenAlex
- Citations by year (recent)
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2025: 21, 2024: 27, 2023: 2Per-year citation counts (last 5 years)
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34Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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