Assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears
Journal:
arXiv
Published Date:
Aug 16, 2024
Abstract
Malaria remains a significant global health challenge, necessitating rapid
and accurate diagnostic methods. While computer-aided diagnosis (CAD) tools
utilizing deep learning have shown promise, their generalization to diverse
clinical settings remains poorly assessed. This study evaluates the
generalization capabilities of a CAD model for malaria diagnosis from thin
blood smear images across four sites. We explore strategies to enhance
generalization, including fine-tuning and incremental learning. Our results
demonstrate that incorporating site-specific data significantly improves model
performance, paving the way for broader clinical application.
