DuSCN-FusionNet: An Interpretable Dual-Channel Structural Covariance Fusion Framework for ADHD Classification Using Structural MRI

DuSCN-FusionNet: An Interpretable Dual-Channel Structural Covariance Fusion Framework for ADHD Classification Using Structural MRI | Signal Canvas | ScienceToStartup

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Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-03-30
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 26
Source count: 3
Coverage: 50%

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Evidence Receipt

Route status: building

Claims: 12

References: 26

Proof: Verification pending

Freshness state: computing

Source paper: DuSCN-FusionNet: An Interpretable Dual-Channel Structural Covariance Fusion Framework for ADHD Classification Using Structural MRI

PDF: https://arxiv.org/pdf/2603.26351v1

Source count: 3

Coverage: 50%

Last proof check: 2026-03-30T21:53:18.036Z

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Watch and verify: DuSCN-FusionNet: An Interpretable Dual-Channel Structural Covariance Fusion Framework for ADHD Classification Using Structural MRI

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Subject: DuSCN-FusionNet: An Interpretable Dual-Channel Structural Covariance Fusion Framework for ADHD Classification Using Structural MRI

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Claim map

Strong 12Mixed 0Weak 0

Evidencepartial
achieving a mean balanced accuracy of 80.59%
Implicationpartial
The abstract explicitly states this performance metric.
Verificationpartial
partial
Evidencepartial
and an AUC of 0.778
Implicationpartial
The abstract explicitly states this performance metric.
Verificationpartial
partial
Evidencepartial
achieving a mean balanced accuracy of 80.59%
Implicationpartial
The abstract explicitly states this performance metric.
Verificationpartial
partial
Evidencepartial
and an AUC of 0.778
Implicationpartial
The abstract explicitly states this performance metric.
Verificationpartial
partial
Evidencepartial
leverages dual-channel Structural Covariance Networks (SCNs) to capture inter-regional morphological relationships.
Implicationpartial
This is a core methodological description in the abstract.
Verificationpartial
partial
Evidencepartial
ROI-wise mean intensity and intra-regional variability descriptors are used to construct intensity-based and heterogeneity-based SCNs
Implicationpartial
This details the construction of the SCNs, a key methodological aspect.
Verificationpartial
partial
Evidencepartial
auxiliary ROI-wise variability features and global statistical descriptors are integrated via late-stage fusion
Implicationpartial
This describes a specific fusion strategy used in the method.
Verificationpartial
partial
Evidencepartial
Grad-CAM is adapted to the SCN domain to derive ROI-level importance scores
Implicationpartial
This describes the interpretability method used.
Verificationpartial
partial
Evidencepartial
The model is evaluated using stratified 10-fold cross-validation with a 5-seed ensemble strategy
Implicationpartial
This details the experimental setup for evaluation.
Verificationpartial
partial
Evidencepartial
DuSCN-FusionNet further achieves precision, recall, and F1-scores of 81.66%, 80.59%, and 80.27%, respectively.
Implicationpartial
These specific performance metrics are explicitly stated in the abstract.
Verificationpartial
partial
Evidencepartial
leverages dual-channel Structural Covariance Networks (SCNs) to capture inter-regional morphological relationships.
Implicationpartial
This is a core methodological description in the abstract.
Verificationpartial
partial
Evidencepartial
ROI-wise mean intensity and intra-regional variability descriptors are used to construct intensity-based and heterogeneity-based SCNs
Implicationpartial
This details the specific inputs for the SCNs, as described in the abstract.
Verificationpartial
partial

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DuSCN-FusionNet: An Interpretable Dual-Channel Structural Covariance Fusion Framework for ADHD Classification Using Structural MRI

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