A Hybrid Model: Multiple Feature Selection Approach Using Transfer Learning for Bacteria Classification
Bibliographic record
Abstract
Analysis of microscope images is an important topic in medical image processing. However, classification of bacteria, which come in different shapes and sizes and are very small structures, based on their morphological structures is a difficult and time-consuming process that cannot be performed by the naked eye. In this study, a hybrid model for bacterial classification is proposed using transfer learning and feature selection methods together. DenseNet201 is used as a feature extractor with the transfer learning approach in the model. The extracted features were selected separately using four different feature selection algorithms and the best features were merged. The best features were trained and classified using Support Vector Machine (SVM). The dataset used was the Digital Image of Bacterial Species (DIBaS) dataset, which contains 33 bacterial species. The dataset was used with 5-fold and 10-fold cross validation, and the average of the two models was used as the evaluation criterion. In the experimental results, 99.78% accuracy, 99.91% precision, 99.88% sensitivity and 99.89% f-1 score were achieved. Thanks to feature selection, the best features that directly affect the classification performance in the dataset are selected. The proposed model can be helpful in making a preliminary diagnosis or a diagnosis in the clinic. Thanks to its fast and accurate classification performance, it can be used for real-time decision making systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".