---
pipeline_tag: image-classification
tags:
- medical
---

# Novel Approach 1

## Stacked Classifier: RF + SVM + XGB

metrics:
- Accuracy: 0.9911734164070612
- Balanced Accuracy: 0.9903422714760236
- MCC: 0.990784932183338
- ROC AUC Score: 0.999934898058849
- F1 Score: 0.9911734164070612
- Jaccard Score: 0.9825012866700978
- Log Loss: 0.033553756349283356
- Precision: 0.9911734164070612
- Recall: 0.9911734164070612



# Novel Approach 2

## Stacked Classifier: RF + SVM + KNN + XGB

metrics:
- Accuracy: 0.9922118380062306
- Balanced Accuracy: 0.9913200369813552
- MCC: 0.9918690348004674
- ROC AUC Score: 0.9999193482927975
- F1 Score: 0.9922118380062306
- Jaccard Score: 0.9845440494590417
- Log Loss: 0.03136301122428542
- Precision: 0.9922118380062306
- Recall: 0.9922118380062306