Detection and Classification of Alzheimer’s Disease Using Deep Learning Technique

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Year : 2025 | Volume :12 | Issue : 01 | Page : –
By
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T. Sharmila Devi,

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Dr. Sivakumar J.,

  1. Student, Department of Electronics and Communication Engineering, St. Joseph’s college of Engineering, Chennai, Tamil Nadu, India
  2. Student, Department of Electronics and Communication Engineering, St. Joseph’s college of Engineering, Chennai, Tamil Nadu, India

Abstract

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It is crucial that people with Alzheimer’s disease (AD) receive a proper diagnosis to begin preventative action before irreparable brain damage develops. Most people who suffer from Alzheimer’s disease (AD), a neurological condition that progresses, are older than 65. The area of interest (ROI) in the hippocampus has been extensively studied for several purposes, including neurological illness research, stress development monitoring, and memory function analysis. Moreover, a connection between Alzheimer’s disease and hippocampus volume shrinkage is shown. On the other hand, several biomarkers are used in the diagnosis of AD, such as tau, phosphorylated tau, amyloid beta (aβ42) protein, and hippocampus volume atrophy. Even though much recent research has used computers to diagnose AD, congenital findings with most of the machine learning strategies. information, such as MRI and PET imaging, we aim to improve diagnostic reliability. The model’s effectiveness had been assessed using standard metrics, and it achieved a high accuracy of 94.7% in classifying individuals into healthy, mild cognitive impairment (MCI), and Alzheimer’s sections. These findings highlight the possibility of deep learning as a tool for programmed and reliable Alzheimer’s recognition, offering significant advantages for clinical diagnostics. Alzheimer’s disease (AD) is a progressive neurodegenerative disease that primarily affects memory, cognition, and behaviour. Early and accurate detection of AD is essential for effective management and treatment planning. Deep learning, one of the most recent developments in artificial intelligence (AI), has the potential to completely transform medical diagnosis.

Keywords: Alzheimer’s disease, deep learning, Initial-stage detection and diagnosis, Sensor Research, Biomarkers, Machine Learning, magnetic resonance imaging

[This article belongs to Recent Trends in Sensor Research & Technology (rtsrt)]

How to cite this article:
T. Sharmila Devi, Dr. Sivakumar J.. Detection and Classification of Alzheimer’s Disease Using Deep Learning Technique. Recent Trends in Sensor Research & Technology. 2025; 12(01):-.
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T. Sharmila Devi, Dr. Sivakumar J.. Detection and Classification of Alzheimer’s Disease Using Deep Learning Technique. Recent Trends in Sensor Research & Technology. 2025; 12(01):-. Available from: https://journals.stmjournals.com/rtsrt/article=2025/view=0

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Regular Issue Subscription Review Article
Volume 12
Issue 01
Received 02/01/2025
Accepted 14/01/2025
Published 22/01/2025
112