Explainable AI for Malaria Diagnosis from Blood Cell Images
Published:Dec 21, 2025 14:55
•1 min read
•ArXiv
Analysis
This research focuses on applying Convolutional Neural Networks (CNNs) for malaria diagnosis, incorporating SHAP and LIME to enhance the explainability of the model. The use of explainable AI is crucial in medical applications to build trust and understand the reasoning behind diagnoses.
Key Takeaways
- •Applies CNNs to blood cell images for malaria diagnosis.
- •Employs SHAP and LIME methods to provide explainability.
- •Aims to improve trust and understanding in medical AI applications.
Reference
“The study utilizes blood cell images for malaria diagnosis.”