Automatic detection of <i>Cryptosporidium</i> in optical microscopy images using YOLOv5<i>x</i>: a comparative study
Bibliographic record
Abstract
Here, a machine learning tool (YOLOv5) enables the detection of Cryptosporidium microorganisms using optical and phase contrast microscope images. The two databases were processed using 520 images (optical microscopy) and 1200 images (phase contrast microscopy). It used Python libraries to label, standardize the size, and crop the images to generate the input tensors to the YOLOv5 network (s, m, and l). It implemented two experiments using randomly initialized weights in optical and phase contrast microscope images. The other two experiments used the parameters for the best training time obtained before and after retraining the models. Metrics used to assess model accuracy were mean average accuracy, confusion matrix, and the F1 scores. All three metrics confirmed that the optimal model used the best epoch of optical imaging training and retraining with phase contrast imaging. Experiments with randomly initialized weights with optical imaging showed the lowest precision for Cryptosporidium detection. The most stable model was YOLOv5m, with the best results in all categories. However, the differences between all models are lower than 2%, and YOLOv5s is the best option for Cryptosporidium detection considering the differences in computational costs of the models.
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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".