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Big Data Mining and Analytics 2023, 6(4): 504-512 https://doi.org/10.26599/BDMA.2022.9020049
Open Access | Issue | Published: 29 August 2023

Diagnosis and Detection of Alzheimer’s Disease Using Learning Algorithm

Show Author's Information Gargi Pant Shukla1,  Santosh Kumar2,  Saroj Kumar Pandey3,  Rohit Agarwal3,  Neeraj Varshney3,  Ankit Kumar3( )
Department of Management, Doon Business School, Dehradun 248001, India
Jaipuria Institute of Management, Jaipur 302033, India
Department of Computer Engineering and Apllications, GLA University, Mathura 281406, India
Keywords:
deep learning, alzheimer’s disease, random forest, XGBoost
Cite this article:
Shukla GP, Kumar S, Pandey SK, et al. Diagnosis and Detection of Alzheimer’s Disease Using Learning Algorithm. Big Data Mining and Analytics, 2023, 6(4): 504-512. https://doi.org/10.26599/BDMA.2022.9020049
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Abstract Full text About this article

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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About this article

Diagnosis and Detection of Alzheimer’s Disease Using Learning Algorithm

Show Author's information Gargi Pant Shukla1, Santosh Kumar2, Saroj Kumar Pandey3, Rohit Agarwal3, Neeraj Varshney3, Ankit Kumar3( )
Department of Management, Doon Business School, Dehradun 248001, India
Jaipuria Institute of Management, Jaipur 302033, India
Department of Computer Engineering and Apllications, GLA University, Mathura 281406, India

Abstract

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%.

Keywords: deep learning, alzheimer’s disease, random forest, XGBoost

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Received: 28 July 2022
Revised: 21 November 2022
Accepted: 28 November 2022
Published: 29 August 2023
Issue date: December 2023

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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