Article: Detection and classification of Alzheimer's using super-resolution algorithm and convolutional neural network Journal: International Journal of Medical Engineering and Informatics (IJMEI) 2025 Vol.17 No.3 pp.207 - 218 Abstract: On the basis of data obtained from brain imaging, a number of different machine learning (ML) methods may be used to categorise Alzheimer's disease (AD). Convolutional neural networks have been suggested for the classification of Alzheimer's disease based on anatomical MRI in more than 30 different studies. Since the frameworks and implementation details of many researchers are not available to the public, it makes them difficult to replicate. This article extracts the green channel initially, which is further enhanced by using super resolution algorithm. Convolutional neural network is applied to the contrast-enhanced image. We used CNN and T1-weighted MRI to broaden open-source solution for Alzheimer's disease categorisation. Preprocessing, classification, and evaluation techniques for deep learning are included in the framework as well as tools for converting ADNI, AIBL, and OASIS data to the standard of the BIDS format. By combining deep learning with radionics, the accuracy of Alzheimer's disease diagnosis is increased. Inderscience Publishers - linking academia, business and industry through research

Title: Detection and classification of Alzheimer's using super-resolution algorithm and convolutional neural network

Authors: T. Senthil Kumar; Ashok Vajravelu; R. Muthalagu; P. Sri Latha

Addresses: Biomedical Engineering, GRT Institute of Engineering and Technology, Tiruttani, India ' Department of Electronics, Faculty of Electrical Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia ' Department of Electronics and Communication, Agni College of Technology, Chennai, India ' Department of Computer Science and Engineering, Aditya Engineering College (A), Surampalem, India

Abstract: On the basis of data obtained from brain imaging, a number of different machine learning (ML) methods may be used to categorise Alzheimer's disease (AD). Convolutional neural networks have been suggested for the classification of Alzheimer's disease based on anatomical MRI in more than 30 different studies. Since the frameworks and implementation details of many researchers are not available to the public, it makes them difficult to replicate. This article extracts the green channel initially, which is further enhanced by using super resolution algorithm. Convolutional neural network is applied to the contrast-enhanced image. We used CNN and T1-weighted MRI to broaden open-source solution for Alzheimer's disease categorisation. Preprocessing, classification, and evaluation techniques for deep learning are included in the framework as well as tools for converting ADNI, AIBL, and OASIS data to the standard of the BIDS format. By combining deep learning with radionics, the accuracy of Alzheimer's disease diagnosis is increased.

Keywords: image classification; convolutional neural network; CNN; Alzheimer's disease classification; magnetic resonance imaging; MRI.

DOI: 10.1504/IJMEI.2025.145847

International Journal of Medical Engineering and Informatics, 2025 Vol.17 No.3, pp.207 - 218

Received: 16 Apr 2022
Accepted: 22 Jul 2022
Published online: 30 Apr 2025 *

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Title: Detection and classification of Alzheimer's using super-resolution algorithm and convolutional neural network

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