| Journal of Visual Artificial Intelligence
Received: 09 August 2026; Revised: 11 September 2026; Accepted: 22 September 2026; Published Online: 24 September 2026.
J. Vis. Artif. Intell., 2026, 1(2), 26108 | Volume 1 Issue 2 (September 2026) | DOI: https://doi.org/10.64189/vai.26108
© The Author(s) 2026
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)
Schizophrenia Diagnosis with a Transparent
Multimodal MRI Fusion Approach for Early Detection
and Clinical Trust
Mohammad Shohelur Rahman
1,*
Ahnaf Tajwar
2
and Tawaz Rahman
3
1
Department of Computer Science and Engineering, Barkal Ragisb Rabeya College, Barkal, Rangamati, 4570, Chittagong, Bangladesh
2
Department of Computer Science and Engineering, Brac University, Badda, Dhaka, 1212, Dhaka, Bangladesh
3
Department of Computer Science and Engineering, American International University, Khilkhet, Dhaka, 1229, Dhaka, Bangladesh
*Email: md.shohelur.rahman17@gmail.com (Mohammad Shohelur Rahman)
Abstract
Schizophrenia affects thought, perception, emotion, and cognition. MRI-based machine learning can help
identify group-level brain patterns, but small datasets and black-box decisions limit confidence in the
predictions. This work proposes a transparent multimodal framework that combines 378 functional network
connectivity (FNC) features from functional MRI (fMRI) with 32 source-based morphometry (SBM) features
from structural MRI (sMRI). The framework uses multiscale convolutional branches, attention-based fusion,
feature ranking, and explainable artificial intelligence (XAI) with layer-wise relevance propagation (LRP) and
Grad-CAM. The MLSP 2014 cohort contains 86 subjects: 40 with schizophrenia and 46 healthy controls. Using a
stratified 80/20 split, 18 subjects were retained for final testing. The proposed MsRNN achieved 83.33%
accuracy, 85.71% precision, 75.00% recall, and an 80.00% F1-score on this held-out set. The 95% Wilson
confidence interval for accuracy was 60.7894.16%, which shows that the point estimate remains uncertain
because the test set is small. LRP andGrad-CAM highlight regions that influenced the fitted model, but these
regions are treated as model explanations rather than validated clinical biomarkers. The results show the
potential of multimodal MRI fusion while also emphasizing the need for larger independent cohorts and
repeated validation.
Keywords: Schizophrenia diagnosis; Brain imaging; Machine learning; AI for psychiatry; Explainable Artificial
Intelligence; Multimodal brain scans.
1. Introduction
Schizophrenia is a severe mental disorder that can affect perception, thought, emo-tion, motivation, and
cognition. Clinical diagnosis is based on symptoms and clinical assessment; no single MRI marker is sufficient
for diagnosis.
[1]
Structural and func-tional neuroimaging can nevertheless reveal group-level brain differences
that may help computational models distinguish patients from healthy controls.
[2]
Structural magnetic resonance imaging (sMRI) describes anatomical properties such as gray-matter
distribution, cortical thickness, and ventricular size. Functional magnetic resonance imaging (fMRI) describes
activity and connectivity patterns, including correlations between functional networks. The two modalities
therefore contain different but potentially complementary information. Combining them can provide a broader
representation of disease-related variation than using either modality alone.
[3]
Machine-learning models can learn complex patterns from neuroimaging data, but high dimensionality, small
sample sizes, and limited interpretability remain important problems.
[4]
A model can achieve good predictive
performance while still relying on unstable or non-clinical features. For this reason, performance and
interpretability should be considered together. Explainable artificial intelligence (XAI) methods can show
which features or image regions contribute to a prediction, but these explanations require cautious
interpretation and independent validation.
This work develops a multimodal schizophrenia-classification pipeline using the MLSP 2014 dataset. FNC
features from fMRI and SBM features from sMRI are com-bined with a multiscale attention-based classifier.
Random-forest feature importance and t-SNE are used for complementary analysis. LRP and Grad-CAM are used
to inspect model explanations. The main contributions are:
1. An integrated multimodal pipeline that uses both FNC and SBM information for binary schizophrenia
classification;
2. A three-branch multiscale feature extractor with attention-based feature fusion before classification;
3. A combined analysis workflow that links predictive performance, feature ranking, low-dimensional
clustering, ICA maps, and two XAI methods; and
4. An uncertainty-aware evaluation that reports the held-out test metrics together with 95% confidence
intervals and explicitly states the limitations caused by the small sample size.
Fig. 1: Overview of the proposed multimodal pipeline. FNC and SBM information derived from fMRI and sMRI is used
for classification, while Grad-CAM and LRP provide post-hoc explanations.
The rest of the paper is organized as follows. Section 2 reviews related neuroimaging and machine-learning
research. Section 3 states the research gap and methodological novelty. Section 4 describes the data,
preprocessing, model, algorithms, and
XAI
methods. Section 5 presents the results and uncertainty
analysis. Section 6 discusses interpretation and limitations, and Section 7 concludes the paper.
2. Related work
Neuroimaging research has reported structural and functional alterations in schizophrenia, including
changes in gray matter, ventricular volume, and large-scale functional networks.
[2]
sMRI provides
anatomical information, whereas resting-state fMRI can represent functional interaction between brain
regions. These modalities are complementary, but their high dimensionality and different statistical
properties make multimodal integration difficult.
Machine-learning methods have been used to classify schizophrenia from MRI-derived features. Yang et
al.
[5]
combined fMRI and genetic information and reported 87.25% accuracy using leave-one-out validation
on 40 subjects. Their result demon-strated the potential value of multimodal information, but the sample
was small and the modalities differed from the present FNCSBM setting. Zhang
et al
.
[6]
used 3D structural
MRI and reported 92.1% accuracy and an AUC of 0.987 on an independent test set. This approach used
only structural MRI. More recently, Bi
et al.
[3]
proposed MultiViT to fuse 3D gray-matter maps with FNC
information and reported an AUC of 0.833. Their cross-attention mechanism improved multimodal fusion
and provided attention-based localization.
Table 1 places the present results beside representative prior work. The values are not directly comparable
because the datasets, modalities, validation procedures, and reported metrics differ. The purpose of the
table is to show where the present work fits in the literature rather than to claim a universal performance
advantage.
The literature therefore shows three recurring gaps. First, small neuroimaging cohorts make performance
estimates uncertain. Second, feature selection can cause
data leakage when it is performed before the
train/test split. Third, visual explanations may be useful but should not automatically be treated as clinical
biomarkers. The present revision addresses these issues by explicitly defining the leakage-safe processing
order, reporting the exact held-out test size, adding statistical uncertainty, and using cautious language for
XAI interpretations.
Table 1: Representative neuroimaging classification work and the position of the present method. Metrics are
reported as stated in the cited papers and are not directly comparable across different datasets and validation
designs.
Work
Modalities
Method
Reported result
Yang et al.
[5]
fMRI + SNP
SVM ensemble + voting
Accuracy 87.25%
Zhang et al.
[6]
sMRI
3D SE-VGG
Accuracy 92.1%; AUC 0.987
Bi et al.
[3]
sMRI + fMRI FNC
Vision transformer + cross-
attention
AUC 0.833
Present work
sMRI SBM + fMRI
FNC
Multiscale branches + attention
+ XAI
Accuracy 83.33%; F1
80.00%
3. Research gap and novelty
The methodological novelty is the integration of four components in one pipeline. First, the method combines
two MRI-derived feature families: 378 FNC features from fMRI and 32 SBM features from sMRI. Second, the
proposed classifier uses three scale-specific feature pathways with 32, 32, and 64 convolutional filters followed
by max pooling. Third, an attention module learns a weight for each scale before the feature maps are
concatenated. Fourth, the predictive output is linked to feature ranking, t-SNE visualization, ICA maps, LRP, and
Grad-CAM. This design therefore connects classification with several complementary forms of interpretation
rather than reporting a prediction score alone.
The novelty should not be interpreted as a claim that the method is universally more accurate than all previous
models. Published results use different datasets and evaluation procedures. The contribution of the present
method is the combined multimodal and explanation-oriented workflow, together with an uncertainty-aware
evaluation on the MLSP cohort.
4. Methodology
Fig. 1 summarizes the complete workflow. The method uses the FNC and SBM features supplied by the MLSP
2014 Schizophrenia Classification Challenge. The processing order is important: subjects are split before any
label-dependent feature selection is fitted. Scaling and SelectKBest are fitted on the training data only and are
then applied to the held-out data. This prevents information from the test labels entering the feature-selection
stage.
4.1 Data description
The MLSP 2014 Schizophrenia Classification Challenge provides multimodal MRI-derived features for
schizophrenia classification.
[7]
The training cohort used here contains 86 subjects: 40 subjects with
schizophrenia and 46 healthy controls. Each subject has 410 features. Of these, 378 are FNC features
derived from resting-state fMRI and 32 are SBM loadings derived from structural MRI. The challenge
also provides ICA brain maps associated with the functional and structural components.
4.2 Data splitting, preprocessing, and leakage control
The reported evaluation uses a stratified 80/20 hold-out split. This gives 68 subjects for model
development and 18 subjects for final testing. The held-out set contains 10 healthy controls and 8 subjects
with schizophrenia, as reflected by the integer counts in the confusion matrices. The held-out subjects are
not used to fit the scaler or the feature-selection procedure.
For each feature
, standardization is performed with the mean
and standard deviation
calculated from
the training subset only:





The same training values

and

are then used to transform the held-out subjects.
SelectKBest with the ANOVA F statistic is used to retain the 100 highest-ranked training features. For feature ,
the score can be written as the ratio between between-class and within-class variability:






(2)
where is the number of classes and is the number of training subjects. Feature ranking is performed
using the training labels only. Pearson correlation is used as a descriptive analysis of linear association:





(3)
-
therefore describes only the validation design supported by the reported results: a stratified held-out test set.
No repeated-cross-validation result is claimed.
Fig. 2: The proposed MsRNN architecture for two-class classification. Each scale-specific branch uses convolutional
feature extraction, followed by attention-based fusion and a final classifier.
4.3 ICA-derived functional and structural features
The MLSP data provide FNC and SBM features derived from ICA-based processing. In ICA, the observed data
matrix X is represented as a mixture of statistically independent sources S:
 (4)
where A is a mixing matrix. An unmixing matrix W estimates the source representation:
 (5)
The challenge documentation describes 28 non-artifactual fMRI ICA components and 32 sMRI ICA components.
FNC values summarize pairwise correlations between functional component time courses, whereas SBM
loadings represent the expression of structural gray-matter ICA maps.
[7]
4.4 Two-class classification
The proposed MsRNN is a binary classifier for schizophrenia and healthy-control labels. As shown in Fig. 2, three
scale-specific pathways are used. Each pathway contains convolutional layers with 32, 32, and 64 filters,
followed by max pooling. The resulting feature maps have the documented size 32 × 32 × 64 before attention-
based fusion.
For an input , the three scaled representations are


(6)
where
denotes the scale. In branch , convolutional features are calculated as





(7)
where
is the nonlinear activation and denotes convolution. After the third convolution, max pooling gives

(8)
The attention module produces a score for each scale:

(9)
which is converted to a normalized weight



(10)
The weighted maps
are concatenated:

 (11)
The fused representation is passed to the final classifier and converted to a schizophrenia probability by a
sigmoid output. Binary cross-entropy is used as the objective:



(12)
Table 2 lists the implementation information that is recoverable from the retained experimental record. This
avoids introducing undocumented hyperparameter values during revision.
Fig. 3: Ranking of the 50 most influential neuroimaging features used for exploratory interpretation. SBM features
dominate the highest ranks, while FNC features also contribute.
Table 2: Documented implementation settings of the proposed model
Item
Setting
Input feature families
378 FNC + 32 SBM
Selected features
Top 100 by training-only ANOVA F score
Input scales
3
Filters per scale branch
32, 32, 64
Post-branch feature map
32 × 32 × 64
Fusion
Learned attention + concatenation
Output
Sigmoid binary probability
Loss
Binary cross-entropy
Final evaluation
Stratified hold-out, 18 subjects
A set of benchmark classifiers (RF, ANN, SVM, KNN, and LR) is evaluated on the same held-out partition using
the same training-only preprocessing principle. Because the archived manuscript package does not contain the
executable training code, software-specific settings that are not documented in the retained record are not
reconstructed or invented in this revision. This limitation is stated explicitly in Section 6.
4.5 Feature importance analysis
A random-forest classifier is used as a complementary feature-ranking tool. The 50 highest-ranked FNC/SBM
features are displayed in Fig. 3. Four of the top five ranked features are SBM features, indicating that structural
information has a strong role in this specific fitted model. This observation is descriptive for the present cohort
and should not be interpreted as proof that structural MRI is generally more informative than fMRI.
The selected features are also visualized using t-distributed stochastic neighbor embedding (t-SNE).
[8]
t-SNE
minimizes the divergence between high-dimensional and low-dimensional neighborhood distributions:





(13)
Fig. 4 shows the three-dimensional embedding. The two classes overlap, which is expected for a heterogeneous
clinical condition and indicates that the selected features do not form perfectly separated groups.
Fig. 4: Three-dimensional t-SNE views of the selected features. The visual overlap between classes indicates that the
feature space is not perfectly separable.
The ICA maps supplied with the challenge are used to relate selected FNC and SBM components to spatial brain
patterns. These visualizations are used for interpretation only; they do not establish clinical biomarkers.
4.6 XAI Approaches
LRP and Grad-CAM are used as post-hoc explanation methods. LRP redistributes a model output backward
through the network so that input regions receive relevance values associated with the prediction.
[9]
A generic
relevance-conservation step can be
written as



(14)
where


is the relevance passed from neuron in layer to neuron in layer .
Grad-CAM uses the gradient of a target class score
with respect to a convolutional feature map
.
[10]
The
weight of feature map is



(15)
and the class activation map is


(16)
These heatmaps are explanations of the fitted model, not direct measurements of disease mechanisms. The
present dataset does not provide enough independent subjects to establish the stability of every highlighted
region. Therefore, the revised interpretation focuses on whether the maps are spatially plausible and whether
the two methods show broadly compatible attention patterns. Quantitative cross-subject explanation stability
is left for future validation on a larger independent cohort.
5. Results
This section reports the held-out classification results, uncertainty of the accuracy estimates, feature-space
visualizations, ICA maps, and XAI outputs.
5.1 Classification performance
Table 3 reports the same performance values as the original manuscript. MsRNN obtained 0.8333 accuracy,
0.8571 precision, 0.7500 recall, and 0.8000 F1-score on the 18-subject held-out test set.
Table 3: Held-out classification performance. A = accuracy, P = precision, R = recall, F1 = F1-score.
Model
A
P
R
F1
MsRNN
0.8333
0.8571
0.7500
0.8000
RF
0.7222
0.7143
0.6250
0.6667
ANN
0.7222
0.6364
0.8750
0.7368
SVM
0.7778
0.8333
0.6250
0.7143
KNN
0.7222
0.8000
0.5000
0.6154
LR
0.7778
0.7500
0.7500
0.7500
Fig. 5:
Original performance visualization for the proposed and benchmark classifiers. The values correspond
to the held-out metrics in Table 3.
Fig. 6: Metric-profile heatmap. The figure provides an additional graphical view of accuracy, precision, recall, and F1-score without
introducing new experimental values.
5.2 Statistical uncertainty of the held-out accuracy
Because only 18 subjects are in the held-out set, a small change in the number of correct predictions produces
a large change in accuracy. We therefore report 95% Wilson binomial confidence intervals for accuracy. MsRNN
correctly classified 15 of 18 subjects, giving 83.33% accuracy with a 95% Wilson interval of 60.7894.16%. SVM
and LR each classified 14 of 18 subjects correctly (77.78%; 95% CI 54.7891.00%), while RF, ANN, and KNN
each classified 13 of 18 correctly (72.22%; 95% CI 49.1387.50%). The intervals overlap substantially.
Therefore, the revised manuscript does not claim a statistically proven superiority of MsRNN over the
benchmark models.
Fig. 7: Held-out accuracy with 95% Wilson confidence intervals. The wide and overlapping intervals show the
uncertainty caused by the small test set.
The original confusion-matrix image contained two percentage inconsistencies for
ANN and KNN. Fig. 8 has
been regenerated from the integer counts implied by the reported accuracy, precision, and recall values. For
MsRNN, 9 of 10 healthy controls and 6 of 8 schizophrenia cases were classified correctly.
5.3
Feature ranking and low-dimensional structure
Fig. 3 shows the 50 highest-ranked features. The strongest positions are mainly SBM features, and four of the
top five ranked features are structural. Fig. 4 shows that the healthy-control and schizophrenia samples are not
fully separated in the t-SNE embedding. This overlap is consistent with the moderate test accuracy and
supports a cautious interpretation of the classification boundary.
5.4 ICA-based spatial visualizations
The main ICA components are shown in Figs. 9 and 10. These maps provide spatial context for functional and
structural components. They are used to visualize pat-terns associated with the fitted model and dataset, not to
establish definitive disease markers.
The structural maps include patterns near frontal and temporal regions and ventricular areas. These
observations are broadly compatible with neuroimaging literature reporting structural differences in
schizophrenia.
[2]
However, the present analysis is exploratory and does not test these regions as independent
biomarkers.
The fMRI maps show heterogeneous patterns of stronger and weaker activation/connectivity across the
displayed slices. Because the present dataset is small and the analysis is model-driven, these patterns are
described as candidate regions for further investigation rather than causal or diagnostic findings.
Fig. 8:
Corrected held-out confusion matrices. Each cell gives the subject count and the percentage
within its actual class.
Fig. 9: Top three fMRI ICA component maps used to inspect functional patterns associated with the classification
analysis.
Fig. 10: Top three sMRI ICA component maps used to inspect structural patterns associated with the classification
analysis.
Fig. 11: Illustrative structural visualization used in the exploratory interpretation. Color overlays show regions
emphasized in the processed maps; they should not be read as independently validated clinical abnormalities.
Fig. 12: Illustrative fMRI visualization showing spatially varying activation/connectivity patterns used for
exploratory interpretation.
5.5 Model interpretability and explanation reliability
Fig. 13 shows LRP heatmaps for schizophrenia and healthy-control examples. Higher-resolution maps provide
more localized relevance patterns than the lower-resolution views in the displayed examples. This improves
visual localization, but it does not by itself demonstrate that the highlighted region is a disease biomarker. Fig.
14 presents the corresponding Grad-CAM visualization. Warm colors indicate regions with larger positive
contributions to the displayed class score. LRP and Grad-CAM provide two different views of model attention.
Their broad spatial agree-ment in the examples is useful as a qualitative check; however, explanation
consistency was not quantified over all subjects. This is a limitation of the present analysis.
Fig. 13:
LRP heatmaps for example predictions. The maps show input regions that receive higher relevance under
the fitted model.
Fig. 14: Grad-CAM example. Warm colors indicate image regions with larger positive contribution to the target class
score. The map explains the model prediction and is not a clinical diagnostic map.
6. Discussion
The results show that multimodal FNC and SBM information can be combined within an attention based
multiscale framework. MsRNN produced the highest point estimate of accuracy in the reported held-out
comparison (83.33%), with precision of 85.71%, recall of 75.00%, and F1-score of 80.00%. The model also
produced a more balanced precisionrecall profile than several baselines. However, the practical difference
between point estimates must be interpreted together with the uncertainty analysis. The 95% confidence
intervals are wide and overlap because the held-out set contains only 18 subjects. The revised paper therefore
treats the performance result as preliminary rather than as proof that MsRNN is statistically better than every
benchmark.
The small cohort is the main limitation. With 86 subjects and hundreds of features, overfitting is a substantial
risk. To reduce leakage, scaling and ANOVA feature selection are defined as training-only operations in the
revised methodology. Even with this safeguard, a single held-out split can be sensitive to which subjects enter
the test set. A larger independent dataset, repeated stratified cross-validation, or nested cross-validation would
provide a stronger estimate of generalization. Because the archived submission package did not include
executable training code or subject-level prediction files, this revision does not invent repeated-run results or
pairwise significance tests that cannot be reconstructed reliably.
Feature ranking and t-SNE provide complementary information. The high-ranked feature list contains a strong
SBM contribution, while the t-SNE plot shows overlap between the two diagnostic groups. These findings
suggest that structural features contribute useful discrimination in this fitted model, but the two groups remain
heterogeneous. The ICA maps give anatomical context to the feature analysis, yet they should be interpreted at
the group/model level.
LRP and Grad-CAM make the model easier to inspect, but post-hoc explanations have their own uncertainty. A
highlighted area indicates that a fitted model used information from that area; it does not prove that the region
is a clinically validated biomarker or a causal mechanism. The present figures provide qualitative examples only.
A stronger XAI validation would calculate explanation consistency across sub-jects and repeated models, for
example by measuring overlap or rank correlation of salient regions and then testing the same regions in an
independent cohort. These steps are important before clinical use.

avoided unless referring to prior validated literature. The displayed regions are described as candidate or
discriminative regions that require external validation. This distinction is especially important because the
present cohort is small and comes from a single challenge dataset.
Finally, reproducibility is limited by missing software-level details in the archived manuscript package. The
revised paper reports all settings that can be recovered reliably, including the number of modalities and
features, selected-feature count, three multiscale branches, filter counts, feature-map size, attention fusion,
binary loss, and held-out sample size. The optimizer, learning rate, batch size, kernel size, software versions,
and random seed should be recovered from the original executable code before final publication if those records
are available.
6. Conclusion
This paper presents a multimodal MRI classification framework that combines fMRI-derived FNC features and
sMRI-derived SBM features with multiscale feature extraction, attention fusion, and XAI. On the 18-subject held-
out test set, MsRNN achieved 83.33% accuracy, 85.71% precision, 75.00% recall, and 80.00% F1-score. The
95% Wilson interval for accuracy (60.7894.16%) shows that the estimate remains uncertain because of the
small sample. LRP and Grad-CAM provide useful model-level visual explanations, but the highlighted regions
require independent validation before they can be interpreted as clinical biomarkers. Future work should use
larger indepen-dent cohorts, repeated or nested validation, quantitative XAI-stability measures, and complete
release of implementation settings and subject-level predictions.
CRediT Author Contribution Statement
Mohammad Shohelur Rahman:
Conceptualization, Methodology, Investigation, Formal analysis,
Visualization, WritingOriginal draft, WritingReview and editing.
Ahnaf Tajwar:
Conceptualization,
Methodology, Investigation, Formal analysis, Visualization, WritingOriginal draft, WritingReview and
editing.
Tawaz Rahman:
Conceptualization, Methodology, Investigation, Formal analysis, Visualization,
WritingOriginal draft, WritingReview and editing. All authors reviewed and approved the final
manuscript and agree to be accountable for the work.
Data Availability Statement

             

Conflict of Interest
The authors received no specific financial support for the research, authorship, or publication of this work.
Funding Declaration
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profit sectors.
Artificial Intelligence (AI) Use Disclosure
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