Location of craniofacial landmarks on X-ray images by employing fuzzy neural network
Bibliographic record
Abstract
Analysis of cephalometric X-rays and measurements of parameters based on detected features play an important role in monitoring the treatment. The analysis is based on location of landmarks that form the basis of linear and angular measurements. Landmarks are difficult to distinguish in the image because their location depends on external forces such as growth, rotation and shifting in the skull X-ray. In this paper we present a new method for estimating locations of the landmarks by employing a fuzzy neural network. This method has a very high non-linear mapping ability, a fast learning time and guarantee of global minima. In the proposed method fuzzy sets are formed from the training data. Using fuzzy linguistics if-then rules, membership degrees are assigned to the extracted features and fed to the network for training utilizing gradient descent scheme. After training the network is tested on estimating the locations of the landmarks on target images (not used for training). The results moreover, are promising.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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 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".