Dental Caries Detection Using Neural Turing Machines (NTM) and High Intensity Color Detection (NTM-HICD) Model
Bibliographic record
Abstract
Dental cavities, caries, or tooth decay are prevalent oral health concerns worldwide.This research proposes an automated approach for dental cavities detection utilizing a Neural Turing Machines (NTM) and High Intensity Color Detection (NTM-HICD) model.These two models process the input samples in a sequence order.NTM is a type of artificial neural network (ANN) architecture that combines neural networks with external memory structures.NTM mainly designed to mimic the ability of a Turing machine to read the interesting patterns from various disease detection.The proposed NTM-HICD system combines the strengths of multiple deep learning algorithms to enhance the accuracy and robustness of dental cavity detection.The design incorporates three primary components: image processing, feature extraction, and classification.Firstly, dental X-rays are processed to enhance the quality of input data.A pre-trained model DeepLabV3+ is used to train on dental dataset.The images are then subjected to effected region extraction to focus on the tooth areas for more targeted analysis.Secondly, a set of diverse feature extraction techniques is applied to capture comprehensive information from the effected regions.Lastly, an ensemble of classifiers, such as support vector machines (SVM), random forests (RF), and deep neural networks (DNN), is employed to leverage the individual strengths of each classifier.The fusion of multiple classifiers allows for improved generalization and enhanced detection performance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".