The use of artificial neural networks to diagnose Alzheimer’s disease from brain images

Fouladi, Saman; Safaei, Ali; Arshad, Noreen Izza; Ebadi, M. J.; Ahmadian, Ali

doi:10.1007/s11042-022-13506-7

Cited by 20 publications

(10 citation statements)

References 139 publications

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“…Among the types of models that are used for biological systems, mathematical models are more popular because they enable the prediction and control of biological systems [ 29 , 30 ].…”

Section: An Introduction To Mathematical Epidemiology and Compartment...mentioning

confidence: 99%

RETRACTED: SIR model for the spread of COVID-19: A case study

Salimipour

Mehraban

Ghafour

et al. 2023

Operations Research Perspectives

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“…Among the types of models that are used for biological systems, mathematical models are more popular because they enable the prediction and control of biological systems [ 29 , 30 ].…”

Section: An Introduction To Mathematical Epidemiology and Compartment...mentioning

confidence: 99%

RETRACTED: SIR model for the spread of COVID-19: A case study

Salimipour

Mehraban

Ghafour

et al. 2023

Operations Research Perspectives

Self Cite

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“…ADNI data, freely available to qualified researchers worldwide, have been used for the development of many of the diagnostic and prognostic methods in AD. For instance, 56% of studies included in a review of the use of artificial neural networks to diagnose AD from brain imaging scans used ADNI data 73 . A number of systematic reviews of models predicting MCI progression to AD dementia reported that ADNI data were used in 67%, 74 85%, 75,76 and 92% 77 of included studies.…”

Section: Adni's Contributions To the Treatment Diagnosis And Predicti...mentioning

confidence: 99%

The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022

Veitch,

Weiner,

Miller

et al. 2023

Alzheimer's & Dementia

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The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co‐pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI‐4 aims to address by enrolling a diverse cohort.

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“…An expected output deviates from the actual time, and cross-entropy reduces and increases. The binary cross-entropy loss was computed as follows if there are 2 types: (6) RMSprop, for example, can be used to update the W variable, which corresponds to a neuron in the IFRCNN as follows: (7) (8)…”

Section: U N C O R R E C T E Dmentioning

confidence: 99%

“…ML is the research of computer simulations that get better on their own with practice (5). ML methods develop a statistical approach that uses sample data, sometimes known as "training data," to produce predictions or decisions of specifi cally trained (6). ML techniques were utilized in a wide range of software, including image identifi cation, clinical issue, and categorization if it would be diffi cult or impractical to perform the necessary procedures using different algorithms (7).…”

Section: Introductionmentioning

confidence: 99%

Alzheimer’ disease prediction and classification using CT images through machine learning

REDDY¹,

KRISHNA²

2023

BLL

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Numerous surveys using different techniques have been conducted in recent years to accurately classify Alzheimer's disease (AD). This research emphasized the identifi cation of AD through neuroimaging data. However, it is important to identify symptoms as soon as possible when the disease-modifying medications function best during infection before a permanent cognitive impairment develops. The use of automated algorithms to detect the early symptom of AD to this information was very important. Machine Learning (ML) has been proposed for the evaluation of various image segmentation and database techniques. In addition, Visual Geometry Group (VGG)-16 & Improved Faster Recurrent Convolutional Neural Network (IFRCNN) method developed for the ImageNet database utilizing the mathematical model based on action recognition as a feature extractor for categorization work. Experiments are being conducted on the Alzheimer's Neuroimaging Initiative (ADNI) dataset, and the proposed system achieves the 98.32 % accuracy level (Tab. 6, Fig. 4, Ref. 34).

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The use of artificial neural networks to diagnose Alzheimer’s disease from brain images

Cited by 20 publications

References 139 publications

RETRACTED: SIR model for the spread of COVID-19: A case study

RETRACTED: SIR model for the spread of COVID-19: A case study

The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022

Alzheimer’ disease prediction and classification using CT images through machine learning

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