Intelligent Identification of Microscopic Visible Components in Leucorrhea Routine

摘要

Leucorrhea routine is a common way of female physiological examination, which is detected by recognizing and counting the visible components in microscopic images. At present, the research in this field is still blank. Based on the deep learning theory, an improved R-CNN model is proposed to realize the intelligent recognition of the visible components in leucorrhea microscopic images. The detection precision of the algorithm is high, reaching 93.6%, and the detection time is 300 ms. The proposed algorithm provides a theoretical basis for the realization of leucorrhea routine automation and intellectualization.