Deep Learning Framework for Schizophrenia Detection and Classification Using Stacked Sparse Autoencoder
Bibliographic record
Abstract
Schizophrenia (SZ) is a complex psychiatric disorder that severely impacts cognitive and social functioning. Conventional diagnosis often depends on subjective clinical evaluation, which may delay intervention and affect treatment outcomes. This paper presents a deep learning framework that employs a Stacked Sparse Auto encoder (SSAE) for the automated detection and classification of SZ. The proposed model extracts compact and discriminative features from multimodal neuroimaging data under sparsity constraints, thereby improving generalization and reducing over fitting. A Softmax classifier maps the learned features into diagnostic categories to distinguish healthy controls from SZ patients. Experimental results on publicly available datasets demonstrate that the model achieves an accuracy of 98.91%, sensitivity of 89.57%, and specificity of 80.58%, outperforming state-of-the-art baselines. The novelty of this work lies in integrating SSAE with multimodal imaging to provide robust, efficient, and clinically applicable decision support for schizophrenia diagnosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".