Design and Analysis of a Highly Sensitive Terahertz Biosensor for Early Cancer Detection Using Silver Surface Plasmon Resonance Metasurfaces and Elastic Reflection Starling Murmuration Equivariant Quantum Decision Networks
Bibliographic record
Abstract
Terahertz (THz) biosensors have emerged as a promising technology for medical diagnostics, particularly for cancer detection, due to their unique capability to interact with biological tissues at the molecular level. This research presents a novel THz biosensor design that combines silver-based surface plasmon resonance metasurfaces with a sophisticated neural network architecture, termed as elastic reflection starling murmuration equivariant quantum decision network. By leveraging reflection equivariant quantum neural networks and integrating them with an elastic decision transformer, this design enhances the sensitivity and specificity of cancer detection by capturing subtle biomolecular interactions. The starling murmuration optimizer extends this process, tweaking the tuning parameters to avoid as many false alarms as possible and to obtain exactly the correct resonant shift for each biomarker change. Its high sensitivity, combined with a quantum-inspired decision process, makes this biosensor a platform for increasing the early diagnostics of tumors compared to traditional approaches. The model also delivers early cancer classifying sensitivity of approximately 99.8%. The suggested structure’s sensitivity can be enhanced up to 275 GHz RIU−1 with the FOM of 3.05 RIU−1 and Q factor of 11.85. The proposed architecture shows potential for scalable applications in clinical settings, aiding in timely diagnosis and potentially improving patient outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".