Title: A novel method to detect and classify Alzheimer using naive Bayes classification algorithm

Authors: C. Ravichandran; T. Senthil Kumar; K. Balaji; Kavitha Sandanam; Chandrasekaran Sivakumaran

Addresses: Department of Electronics and Communication Engineering, GRT Institute of Engineering and Technology Tamil Nadu, India ' Biomedical Department, GRT Institute of Engineering and Technology, Tamil Nadu, India ' Department of Electronics and Communication Engineering, GRT Institute of Engineering and Technology, Tamil Nadu, India ' Department of Electronics and Communication Engineering, Veltech Multitech Dr. Rangarajan, Dr. Sakunthala Engineering College, Vel Nagar, Chennai, Tamil Nadu 600062, India ' Photon Technologies, 58, S. Usman Road, Kannammapet, T. Nagar, Chennai, Tamil Nadu 600017, India

Abstract: Digital medical imaging technology has been more accessible to the general people in recent years. This study describes a software strategy for detecting brain irregularities to diagnose Alzheimer's disease. The suggested method creates a 3D model of the brain using MRI slices. This method is more precise and reliable. In this research, we proposed two strategies. To extract radiological data from MRI images, image processing and machine learning were utilised, and a deep learning approach was applied to analyse the condition of Alzheimer's disease. The algorithm normalises and removes the skull from the MRI images in the first phase. Using a modified K-Means approach, the image is split into white matter (WM), grey matter (GM), and black holes (BH). The classifier is trained using the training data to predict the test data. The characteristics are defined using naive Bayes to create a classification model.

Keywords: Alzheimer's disease; magnetisation resonance image; MRI; mild cognitive impairment; magnetic resonance imaging; deep learning; residual neural network.

DOI: 10.1504/IJMEI.2024.139886

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.4, pp.374 - 383

Received: 27 Dec 2021
Accepted: 03 Apr 2022
Published online: 09 Jul 2024 *

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