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Multimodal classification of Alzheimer's disease and mild cognitive impairment using custom MKSCDDL kernel over CNN with transparent decision-making for explainable diagnosis

Multimodal classification of Alzheimer's disease and mild cognitive impairment using custom MKSCDDL kernel over CNN with transparent decision-making for explainable diagnosis
Multimodal classification of Alzheimer's disease and mild cognitive impairment using custom MKSCDDL kernel over CNN with transparent decision-making for explainable diagnosis
The study presents an innovative diagnostic framework that synergises Convolutional Neural Networks (CNNs) with a Multi-feature Kernel Supervised within-class-similar Discriminative Dictionary Learning (MKSCDDL). This integrative methodology is designed to facilitate the precise classification of individuals into categories of Alzheimer's Disease, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) statuses while also discerning the nuanced phases within the MCI spectrum. Our approach is distinguished by its robustness and interpretability, offering clinicians an exceptionally transparent tool for diagnosis and therapeutic strategy formulation. We use scandent decision trees to deal with the unpredictability and complexity of neuroimaging data. Considering that different people's brain scans are different, this enables the model to make more detailed individualised assessments and explains how the algorithm illuminates the specific neuroanatomical regions that are indicative of cognitive impairment. This explanation is beneficial for clinicians because it gives them concrete ideas for early intervention and targeted care. The empirical review of our model shows that it makes diagnoses with a level of accuracy that is unmatched, with a classification efficacy of 98.27%. This shows that the model is good at finding important parts of the brain that may be damaged by cognitive diseases.
2045-2322
Adarsh, V.
a847847c-cb23-4eb4-b06b-ae6ad7e6fbc6
Gangadharan, G.R.
8bfd2f88-da93-4ecb-b26b-62cd5fd11b58
Fiore, Ugo
115628d4-8528-4731-ae45-9fe118fa88f5
Zanetti, Paolo
099f13c2-37d1-414c-b6d2-f02a1cd43307
Adarsh, V.
a847847c-cb23-4eb4-b06b-ae6ad7e6fbc6
Gangadharan, G.R.
8bfd2f88-da93-4ecb-b26b-62cd5fd11b58
Fiore, Ugo
115628d4-8528-4731-ae45-9fe118fa88f5
Zanetti, Paolo
099f13c2-37d1-414c-b6d2-f02a1cd43307

Adarsh, V., Gangadharan, G.R., Fiore, Ugo and Zanetti, Paolo (2024) Multimodal classification of Alzheimer's disease and mild cognitive impairment using custom MKSCDDL kernel over CNN with transparent decision-making for explainable diagnosis. Scientific Reports, 14 (1), [1774]. (doi:10.1038/s41598-024-52185-2).

Record type: Article

Abstract

The study presents an innovative diagnostic framework that synergises Convolutional Neural Networks (CNNs) with a Multi-feature Kernel Supervised within-class-similar Discriminative Dictionary Learning (MKSCDDL). This integrative methodology is designed to facilitate the precise classification of individuals into categories of Alzheimer's Disease, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) statuses while also discerning the nuanced phases within the MCI spectrum. Our approach is distinguished by its robustness and interpretability, offering clinicians an exceptionally transparent tool for diagnosis and therapeutic strategy formulation. We use scandent decision trees to deal with the unpredictability and complexity of neuroimaging data. Considering that different people's brain scans are different, this enables the model to make more detailed individualised assessments and explains how the algorithm illuminates the specific neuroanatomical regions that are indicative of cognitive impairment. This explanation is beneficial for clinicians because it gives them concrete ideas for early intervention and targeted care. The empirical review of our model shows that it makes diagnoses with a level of accuracy that is unmatched, with a classification efficacy of 98.27%. This shows that the model is good at finding important parts of the brain that may be damaged by cognitive diseases.

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e-pub ahead of print date: 20 January 2024
Published date: 20 January 2024

Identifiers

Local EPrints ID: 495870
URI: http://eprints.soton.ac.uk/id/eprint/495870
ISSN: 2045-2322
PURE UUID: e1e2edf5-2596-41cd-9739-a758b2c2a586
ORCID for V. Adarsh: ORCID iD orcid.org/0000-0002-2134-5126

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Date deposited: 26 Nov 2024 17:43
Last modified: 27 Nov 2024 03:10

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Contributors

Author: V. Adarsh ORCID iD
Author: G.R. Gangadharan
Author: Ugo Fiore
Author: Paolo Zanetti

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