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Automated Classification of Alzheimer’s Disease Stages Using T1-Weighted sMRI Images and Machine Learning

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Proceedings of Congress on Control, Robotics, and Mechatronics (CRM 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 364))

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Abstract

Alzheimer’s disease is the most common forms of dementia. Dementia is the general term for cognitive decline severe enough to impede with daily activities. Early diagnosis of Alzheimer’s disease is important for slowing or stopping the disease’s development, and experts can start preventive treatment right away. The experts must be capable of identifying Alzheimer’s disease in its earliest and most challenging phases. The fundamental objective of this study is to create a machine learning model that can automatically diagnose disease using MRI, a widely used diagnostic tool. This research employed structural MRI to find out the difference between patients with Alzheimer’s disease (AD), stable mild cognitive impairment (sMCI), progressing mild cognitive impairment (pMCI), and normal cognitive functioning (CN). In this research paper, machine learning models, namely SVM, RF, DT, and CNN, are used for multi-class classification. CNN obtained the highest testing accuracy of 88.84% among the four models, with a precision of 80.42%, a recall of 73.17%, and an F1-score of 76.62% for the CN versus sMCI versus pMCI versus AD multi-class classification.

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Authors and Affiliations

  1. University Institute of Engineering and Technology, Panjab University, Chandigarh, 160014, India

    Nand Kishore & Neelam Goel

Authors
  1. Nand Kishore
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  2. Neelam Goel
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Correspondence to Neelam Goel .

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Editors and Affiliations

  1. Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, India

    Pradeep Kumar Jha

  2. Department of Mechanical Engineering, Rajasthan Technical University, Kota, Rajasthan, India

    Brijesh Tripathi

  3. Department of Mechanical and Mechatronic Engineering, UCSI University, Cheras, Kuala Lumpur, Malaysia

    Elango Natarajan

  4. Department of Computer Engineering, Rajasthan Technical University, Kota, Rajasthan, India

    Harish Sharma

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Kishore, N., Goel, N. (2024). Automated Classification of Alzheimer’s Disease Stages Using T1-Weighted sMRI Images and Machine Learning. In: Jha, P.K., Tripathi, B., Natarajan, E., Sharma, H. (eds) Proceedings of Congress on Control, Robotics, and Mechatronics. CRM 2023. Smart Innovation, Systems and Technologies, vol 364. Springer, Singapore. https://doi.org/10.1007/978-981-99-5180-2_28

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  • DOI: https://doi.org/10.1007/978-981-99-5180-2_28

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-5520-6

  • Online ISBN: 978-981-99-5180-2

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