Utilizing Deep Learning and SVM Models for Schizophrenia Detection and Symptom Severity Estimation Through Structural MRI
Bibliographic record
Abstract
The automated diagnosis of schizophrenia utilizing Magnetic Resonance Imaging (MRI) has been the subject of numerous investigations, the majority of which have primarily directed their focus towards disorder detection.This study, however, aims to transcend detection, endeavoring to estimate the severity of schizophrenia symptoms by leveraging structural MRI data.Such capabilities are anticipated to enhance the monitoring of treatment efficacy, guide clinical decision-making, and ultimately contribute to improved schizophrenia management.MRI datasets for schizophrenia patients (23) and control subjects (20) were sourced from the OpenNeuro database.Each structural MRI was processed to extract a grayscale image, which was then segmented into White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF).Statistical attributes-such as standard deviation, moment, and skewness-were derived from each segment to form feature representations of the grayscale images.An SVM with a linear kernel was trained, distinguishing schizophrenia subjects from healthy controls.Furthermore, for the schizophrenia subjects, the sums of their respective Scale for the Assessment of Positive Symptoms (SAPS) and Scale for the Assessment of Negative Symptoms (SANS) scores were computed.A twelve-layer artificial neural network (ANN) was then trained to estimate these symptom severity scores.The SVM model achieved optimal classification accuracy at 81.8%, while the ANN demonstrated a correlation coefficient of 0.811 and a mean absolute error of 1.44 on the validation dataset.This performance surpasses that of a comparable study estimating schizophrenia symptom severity from electroencephalogram (EEG) data, which yielded correlation coefficients ranging from -0.6 to -0.702.The paper concludes with a proposed software architecture for practical application of these findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".