Enhancing blood cell classification using an explainable transformers-based ensemble learning

Key Points

• The approach significantly improves blood cell classification accuracy, showing a 15% increase over traditional methods.
• Key evidence includes improved F1 scores resulting from ensemble models across multiple datasets.
• Analysis includes a comprehensive evaluation of transformers applied within ensemble learning frameworks.
• This technique highlights the need for further research into explainability in machine learning models.