Promoted pathogen detection method and equipment based on improved cyclic steering filtering algorithm, and storage medium

Abstract

The invention relates to an improved pathogen detection method and device based on an improved cyclic steering filtering algorithm and a storage medium. The method comprises the following steps: S1, constructing a pathogen training set for detecting spores and bud spores; s2, training an artificial intelligence target detection model for detecting pathogens of spores and blastospores based on the pathogen training set of spores and blastospores constructed in S1; s3, carrying out filtering operation on the original image based on an improved cyclic steering filtering algorithm; and S4, detecting the pathogen in the input image: carrying out filtering operation on the input image according to the step S3, and then inputting the filtered image into the artificial intelligence target detection model of the pathogen trained in the step S2 for pathogen detection to obtain the position information of the pathogen. According to the invention, the algorithm based on improved cyclic guide filtering is adopted, and the processed image is input into the deep learning target detection model based on the convolutional neural network, so that the detection rate of pathogens is effectively improved.

Description

technical field [0001] The invention relates to a method for detecting pathogens in the microecology of the female reproductive tract, in particular to an improved pathogen detection method, equipment and storage medium based on an improved loop-guided filtering algorithm. Background technique [0002] Spores and blastospores are common pathogenic pathogens in the female reproductive tract. Under certain conditions, they invade the female lower reproductive tract and cause inflammation of the skin and mucous membranes of the vulva, causing female fungal vaginitis, also often called fungal vaginitis. The detection rate of spores plays a vital role in the diagnosis of female mycotic vaginitis. At present, the Gram staining method for biological specimens of female reproductive tract secretions combined with the morphological examination of pathogens under a microscope with a 100-fold high-power objective lens is the most commonly used inspection method, and it is also the gold...

AI Technical Summary

Problems solved by technology

However, the common loop-guided filtering has the following problems: First, the common loop-guided filtering has three parameters, which are the filter window size, the variance of the color difference and the variance of the distance difference, but the same set of parameters cannot be used for different images. At the same time, the desired effect can be achieved, that is, for different images, three different parameters need to be adjusted separately to achieve the desired results. Even for the same image, these parameters often need to be obtained from multiple debugging experiments, so the general The loop-guided filtering method is cumbersome and has no versatility in detecting and identifying pathogens in the special scenario of Gram-stained microscopic images of female genital secretion biological specimens

The second is that after using ordinary loop-guided filtering, although the background in the image is blurred, some of the pathogens in the image become clear and some become blurred, and even the shape of the pathogen cannot be clearly distinguished. The overall image and the surrounding area of ​​​​the pathogen The overall background blur effect is not good, and the edge contours of other objects can still be seen; third, there are situations where pathogens are interfered by other objects, or even connected to other objects, which affects the shape of pathogens, which is likely to cause missed detection of pathogens

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