LIME-Enabled Investigation of Convolutional Neural Network Performances in COVID-19 Chest X-Ray Detection
Bibliographic record
Abstract
The Coronavirus Disease (COVID-19) has caused millions of casualties across the globe. One inexpensive and noninvasive screening method for COVID-19 is the analysis of chest X-ray (CXR) images for pathological features in the lungs. These features are difficult to detect by humans, but convolutional neural networks (CNN) have proven effective at extracting them. This paper uses four ImageNet-pre-trained CNNs: VGG16, DenseNet201, ResNet50, and EfficientNetB3 to perform transfer learning to a task of COVID-19 CXR image detection on a dataset containing COVID-19, healthy, and viral pneumonia CXR images. We compare the performance of the retrained CNNs using standard measures and investigate the features they use for their predictions using local interpretable model-agnostic explanations (LIME). The networks are retrained on two classification tasks: Task 1 consists of classifying healthy and COVID-19 CXR images and task 2 consists of classifying viral pneumonia and COVID-19 CXR images. We find that DenseNet201 and VGG16 achieve higher accuracies than ResNet50 and EfficientN etB3 in both tasks. However, the LIME explanations reveal that VGG16 does not learn disease-relevant features in the lungs, while DenseNet201, ResNet50, and EfficientNetB3 use regions in the lungs to make their predictions. This observation is reinforced by comparing LIME explanations with ground-truth lung regions on an unseen dataset. The prospect of using “black box” deep neural networks for automatic screening of CXRs for COVID-19 can be improved with LIME-enabled investigations of model performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".