Detection of Alzheimer's Disease Stages Based on Deep Learning Architectures from MRI Images

Antony, Febin; Anita, H B; George, Jincy A

doi:10.1201/9781003315452-4

Cited by 2 publications

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“…In a neurodegenerative form of dementia, AD starts with mild cognitive impairment (MCI) and gradually gets worse. It affects brain cells, induces memory loss, thinking skills, and hinders performing simple tasks [3,4]. Therefore, AD is a progressive multi-faceted neurological brain disease.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approach for Early Detection of Alzheimers Disease

Dandwate

2024

IJRASET

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Alzheimer’s disease (AD) is a chronic, irreversible brain disorder, no effective cure for it till now. However, available medicines can delay its progress. Therefore, the early detection of AD plays a crucial role in preventing and controlling its progression. The main objective is to design an end-to-end framework for early detection of Alzheimer’s disease and medical image classification for various AD stages. A deep learning approach, specifically convolutional neural networks (CNN), is used in this work. Four stages of the AD spectrum are multi-classified. Furthermore, separate binary medical image classifications are implemented between each two-pair class of AD stages.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Approach for Early Detection of Alzheimers Disease

Dandwate

2024

IJRASET

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“…There have been several successful applications of computational intelligence-based classi cation systems in medical diagnosis. For example, researchers have developed algorithms that can accurately diagnose Alzheimer's disease based on data from brain imaging and cognitive tests [13]. Additionally, researchers have used computational intelligence-based classi cation systems to identify genetic markers for various diseases, including cancer and heart disease.…”

Section: Introductionmentioning

confidence: 99%

Computational Intelligence-Based Classification System for The Diagnosis of Memory Impairment in Psychoactive Substance Users

Zhu

2023

Preprint

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Computational intelligence techniques have emerged as a promising approach to the diagnosis of various medical conditions, including memory impairment. The increasing abuse of psychoactive drugs presents a global public health burden, as repeated exposure to these substances can cause neurodegeneration, premature aging, and negatively affect memory impairment. Although the basis for these neurotoxic effects has not been fully elucidated, compelling evidence has shown that dysregulation of neurotransmission, disruption of mitochondrial function and dynamics, impairment of neuroimmunomodulation, and epigenetic changes result from many psychoactive substances. (eg, alcohol, cannabis, opiates, amphetamines and cocaine). Computational intelligence used in this study to classify the brain image from MRI or CT scan and show the effective of dose ratio on the health with time of treatment, diagnosis of memory impairment in psychoactive substance users. Understanding the neurotoxic profiles of psychoactive substances and the underlying pathways is assumed to be of great importance towards improved risk assessment and treatment of substance use disorders. As such, we found the brain diagram and its effect on drug-induced cellular and molecular mechanisms. thus we conclude that good classification in such field may save human life or early detection of memory impairment via classification of brain cells and tissues.

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Detection of Alzheimer's Disease Stages Based on Deep Learning Architectures from MRI Images

Cited by 2 publications

References 0 publications

Deep Learning Approach for Early Detection of Alzheimers Disease

Deep Learning Approach for Early Detection of Alzheimers Disease

Computational Intelligence-Based Classification System for The Diagnosis of Memory Impairment in Psychoactive Substance Users

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