DoubleSENeXt: Investigations on Enchondroma Detection
Bibliographic record
Abstract
There are various deep learning models used to solve computer vision problems, with convolutional neural networks (CNNs) and transformers being commonly employed.However, these models have typically proposed by technological giants, and most researchers have relied on them.In this research, we aim to propose a new generation CNN model, termed Double Squeeze-and-Excitation Network (DoubleSENeXt), to address the stagnation in the development of new models.In this study, two new image classification models have been introduced: (i) DoubleSENeXt and (ii) an Exemplar Deep Feature Engineering (EDFE) model.The proposed DoubleSENeXt consists of four main stages: (1) stem, (2) main, (3) downsampling, and (4) output stages.Additionally, we have presented a lightweight version of the proposed DoubleSENeXt.The EDFE model comprises three main phases: (i) feature extraction with the pretrained DoubleSENeXt, (ii) feature selection using Cumulative Weighted Iterative Neighborhood Component Analysis (CWINCA), and (iii) classification with the tkNN algorithm-based k-nearest neighbors.Both new models have been applied to a newly collected enchondroma image dataset for classification.Both models achieved over 92% test classification accuracy on this dataset, with the proposed DoubleSENeXt reaching 92.15% test classification accuracy, and the EDFE model further improving this accuracy to 97.67%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".