Fuzzy Deep Learning for the Diagnosis of Alzheimer's Disease: Approaches and Challenges

Tanveer, M.; Sajid, M.; Akhtar, M.; Quadir, A.; Goel, T.; Aimen, A.; Mitra, S.; Zhang, Y-D; Lin, C. T.; Ser, J. Del

doi:10.1109/tfuzz.2024.3409412

IEEE Trans. Fuzzy Syst.

2024

DOI: 10.1109/tfuzz.2024.3409412

|View full text |Cite

Fuzzy Deep Learning for the Diagnosis of Alzheimer's Disease: Approaches and Challenges

M. Tanveer,

M. Sajid,

M. Akhtar

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 100 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Enhancing Talent Retention in Work 4.0 Era: An Improved Fuzzy Analytical Hierarchy Process and Fuzzy Decision-Making Trial and Evaluation Laboratory Methodology

Lin,

Wang

2024

Int. J. Fuzzy Syst.

View full text Add to dashboard Cite

Enhancing Talent Retention in Work 4.0 Era: An Improved Fuzzy Analytical Hierarchy Process and Fuzzy Decision-Making Trial and Evaluation Laboratory Methodology

Lin,

Wang

2024

Int. J. Fuzzy Syst.

View full text Add to dashboard Cite

Multimodal Alzheimer’s disease classification through ensemble deep random vector functional link neural network

Henríquez,

Araya

2024

PeerJ Computer Science

View full text Add to dashboard Cite

Alzheimer’s disease (AD) is a condition with a complex pathogenesis, sometimes hereditary, characterized by the loss of neurons and synapses, along with the presence of senile plaques and neurofibrillary tangles. Early detection, particularly among individuals at high risk, is critical for effective treatment or prevention, yet remains challenging due to data variability and incompleteness. Most current research relies on single data modalities, potentially limiting comprehensive staging of AD. This study addresses this gap by integrating multimodal data—including clinical and genetic information—using deep learning (DL) models, with a specific focus on random vector functional link (RVFL) networks, to enhance early detection of AD and mild cognitive impairment (MCI). Our findings demonstrate that ensemble deep RVFL (edRVFL) models, when combined with effective data imputation techniques such as Winsorized-mean (Wmean), achieve superior performance in detecting early stages of AD. Notably, the edRVFL model achieved an accuracy of 98.8%, precision of 98.3%, recall of 98.4%, and F1-score of 98.2%, outperforming traditional machine learning models like support vector machines, random forests, and decision trees. This underscores the importance of integrating advanced imputation strategies and deep learning techniques in AD diagnosis.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Fuzzy Deep Learning for the Diagnosis of Alzheimer's Disease: Approaches and Challenges

Cited by 2 publications

References 100 publications

Enhancing Talent Retention in Work 4.0 Era: An Improved Fuzzy Analytical Hierarchy Process and Fuzzy Decision-Making Trial and Evaluation Laboratory Methodology

Enhancing Talent Retention in Work 4.0 Era: An Improved Fuzzy Analytical Hierarchy Process and Fuzzy Decision-Making Trial and Evaluation Laboratory Methodology

Multimodal Alzheimer’s disease classification through ensemble deep random vector functional link neural network

Contact Info

Product

Resources

About