Bibliographic record
Abstract
Since the traditional manual method of classifying lung cancer CT photos is very time-consuming, a new automated, highly accurate classification method is urgently needed. This has led to the use of the Convolutional Neural Network (CNN) for image classification and recognition as the best choice in the medical field. However, due to the problem of vanishing gradient when the layer depth of these models is too deep, the gradient will be vanishingly small and this leads to extremely low learning efficiency, which may even be close to 0. In this paper, a reimplemented CNN model based on ResNet is used to improve low learning efficiency. Three different CNNs are also compared and illustrate that the deeper neural networks have better learning efficiency and higher accuracy for classifying lung CT images. Specifically, LeNet, AlexNet, and ResNet are reimplemented, where the first two CNNs represent traditional models. By comparing the accuracy of the three models, we conclude that the traditional model is sufficient for the task of classifying lung cancer models and has an acceptable accuracy rate when the number of training sessions reaches a certain level, but it is not competitive in learning efficiency and accuracy for the same number of training sessions compared to the subsequent models with more neurons.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".