Quantum machine learning prediction model for retinal conditions: performance analysis
Bibliographic record
Abstract
Quantum machine learning by superposition and entanglement, for disease categorization utilizing OCT images will be discussed in this paper. To the best of our knowledge, this is the first application of the quantum computing element in a neural network model for classifying ophthalmological disease. The model was built and tested with PennyLane (PennyLane.ai), an open-source software tool based on the concept of quantum differentiable programming. The model training circuit functioning was tested on an IBM 5 qubits system “ibmq_belem” and a 32 qubits simulator “ibmq. qasm_simulator”. A hybrid quantum and classical model with a 2 qubit QNode converted layer with operations such as Angle Embedding, BasicEntanglerLayers and measurements were the internal operations of the qlayer. Drusen, Choroidal neovascularization (CNV), and Diabetic macular edema (DME) OCT images formed the abnormal/disease class. The model was trained using 414 normal and 504 abnormal labelled OCT scans and the validation used 97 and 205 OCT scans. The resulting model had an accuracy of 0.95 in this preliminary 2-class classifier. This study aims to develop a 4-class classifier with 4 qubits and explore the potential of quantum computing for disease categorization. A preliminary performance analysis of quantum Machine Learning, the steps involved, and operational details will be discussed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".