Explainable Artificial Intelligence (XAI): Classification of Medical Thermal Images of Neonates Using Class Activation Maps
Bibliographic record
Abstract
In order to determine the health status of the neonates, studies focus on either statistical behavior of the thermograms' temperature distributions, or just correct classifications of the thermograms.However, there exists always a lack of explain-ability for classification processes.Especially in the medical studies, doctors need explanations to assess the possible results of the decisions.Presenting our new study, how Convolutional Neural Networks (CNNs) decide the health status of neonates has been shown for the first time by using Class Activation Maps (CAMs).VGG16 which is one of the pre-trained models has been selected as a CNN model and the last layers of the VGG16 have been tuned according to CAMs.When the model was trained for 50 epochs, train-validation accuracies reached over 95% and test sensitivity-specificity were obtained as 80.701%-96.842%respectively.According to our findings, the CNN learns the temperature distribution of the body by mainly looking at the neck, armpit, and abdomen regions.The focused regions of the healthy babies are armpit and abdomen whereas of the unhealthy babies are neck and abdomen regions.Thus, we can say that the CNN focuses on dedicated regions to monitor the neonates and decides the health status of the neonates.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".