Suqiang Ma

This paper improves sickle cell disease morphology analysis by combining a YOLO detector with a DenseNet classifier for accurate detection and classification of red blood cell morphologies in full-scope microscopy images.

This study introduces a framework for automatically classifying circulating blood cell clusters using flow cytometry images, achieving over 95% accuracy in cluster classification and phenotype identification.