Breast Tumor Computer-aided Diagnosis using Self-Validating Cerebellar Model Neural Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Breast cancer is becoming a leading cause of death among women in the world. However, it is confirmed that early detection and accurate diagnosis of this disease can ensure a long survival of the patients. This study proposes a self-validation cerebellar model articulation controller (SVCMAC) neural network which can yield high accuracy of predication and low false-negative rate for breast cancer diagnosis. With its self-validation unit, the SVCMAC neural network has higher classification accuracy than the conventional CMAC neural network. The parameters of the receptive-field basis function and the weights are all updated first by training data, and the most suitable parameters are then chosen through the self-validation algorithm to retrain the neural network for better performance. Experimental results provide evidence that the SVCMAC neural network has a higher classification accuracy when compared with the BP neural network, LVQ neural network and CMAC neural network.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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 it