Effects of Training Parameters of AlexNet Architecture on Wound Image Classification
Bibliographic record
Abstract
Deep learning is more extensively used in image analysis-based classification of wounds with an aim to facilitate the monitoring of wound prognosis in preventive treatments.In this paper, the classification success of AlexNet architecture in pressure and diabetic foot wound images is discussed.Optimizing training parameters in order to increase the success of Convolutional Neural Network (CNN) architectures is a frequently discussed problem.This paper comparatively examines the effects of optimization of the training parameters of CNN architecture on classification success.The paper examines how the optimizer algorithm, mini-batch size (MBS), maximum epoch number (ME), learning rate (LR), and LearnRateSchedule (LRS) parameters, which are among the training parameters used in combination in architectural training, perform at different values.The best results were obtained with an accuracy of 95.48% at the 10 e-4 value of the LR parameter.When the changes in the evaluation metrics during the parameter optimization experiments were examined, it was seen that the LR parameter produced optimum values at 10 e-4 .As a result, when the Accuracy metric and standard deviations were examined, it was determined only with the LR parameter.No general conclusion could be reached regarding the other parameters.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".