Patent ID: 11468693

Abstract:
The present invention relates to the field of medical technology, and more particularly, to a digital image classification method for cervical fluid-based cells based on a deep learning detection model. The method comprises the following steps: selecting and labeling positions and categories of abnormal cells or biological pathogens in a digital image of cervical liquid-based smears; performing data normalization processing on the digital image of the cervical liquid-based smears; performing model training to obtain a trained Faster-RCNN model by taking the normalized digital image of the cervical liquid-based smears as an input, and the labeled position and category of each abnormal cell or biological pathogen as an output; and inputting an image to be recognized into the trained model and outputting a classification result. The method provided by the embodiment of the present invention can achieve the following advantages: abnormal cells or biological pathogens in a cervical cytological image are positioned; the abnormal cells or biological pathogens in the cervical cytological image are classified; and slice-level diagnostic recommendations are derived by recognizing the positioned abnormal cells or biological pathogens.