The application discloses a method for
typing and identifying
malaria parasites based on multi-scale
feature extraction and confidence weighting, which combines the advantages of thick
blood film and thin
blood film dual-mode recognition on the basis of the Ultralytics YOLOv11 model. First, the
malaria parasite characteristic digital enhancement modulation strengthens the features, and multi-scale
feature extraction and result cleaning are respectively performed on thick blood films (adapted to 9 types of labels) and thin blood films (adapted to 18 types of labels). Then, based on the confidence and quantity weighting rules, preliminary determination of thick
blood film positive and negative and preliminary determination of thin blood film
typing are completed. Finally, through cross-blood film
feature fusion and comprehensive weighted determination, positive and negative and
typing confirmation are realized, and artificial experience digital rules are integrated. The method solves the limitations of single blood film recognition, takes into account the screening sensitivity and identification accuracy, and improves the efficiency and accuracy of
malaria parasite recognition and typing.