Study On The Technique Of Assisted Interpretation Of Cervical Smear Pathological Cells

With the rapid growth of China's economic level,the country and individuals are more concerned about the development of people's livelihood.Cervical cancer,as one of the malignant tumors that threaten women's health and even life,has naturally received widespread attention.One of the most common clinical methods of cervical cancer is cervical smear,or cervical cytology.Its disadvantage is that the diagnosis depends too much on the personal judgment of the medical worker,the uneven doctor-patient ratio and the low diagnosis efficiency.Based on this,the study assists in the completion of the interpretation of cervical pathological cells through automated means,which can detect cervical precancerous lesions in a more timely manner,thereby improving the diagnosis efficiency and diagnosis accuracy of medical workers.The object of this research is cervical pathological cells.Due to the interference caused by the sampling and preparation of cervical smears,there are interference items and cell differences in the pathological cell image.Therefore,the research is conducted on different cell morphologies,including cervical smears.Sample collection and cell image pre-processing,cell image segmentation,feature extraction and pathological cell identification,etc.His main research work includes:First,collect the pathological cells of the cervical smear under the microscope to complete the image preprocessing,including grayscale,image denoising,image enhancement and image sharpening.Second,comparing the principles and effects of the four cell segmentation techniques,according to the grayscale characteristics of the image,the Otsu-based threshold method is used to extract the nuclear region of the image,and the superpixel segmentation method SLIC and the improved labeled watershed method are used to extract the cell body region.Finally,for the complex image of adhesion and overlap,an improved pit detection algorithm is used to complete the separation of cytoplasm.Third,based on the image feature extraction technology,the morphological and chromatic features of cervical cell images are studied and extracted,and according to the internal connection of the features,the nuclear and cytoplasmic ratio and eccentricity characteristics of the cell area are obtained to extract complex texture features For example,based on the features of GLCM and LBP-related features,the information gain method is used to filter the features,and more effective multi-dimensional features are selected as feature sets for classification research.Fourth,for the classification and recognition technology of cervical cell images,this paper studies the BP neural network,including parameter design,activation function selection and improvement methods,compares the loss function curve in the tuning process,selects an initial learning rate of 0.065 for training,and The network connection weights were adjusted,and finally the average recognition rate of classification recognition was 91.4%,and the average detection time of each category was reduced to about 1s through CUD A acceleration.