Simple neural network-based automatic AV analysis method and system
A neural network and analysis method technology, applied in the field of micro-ecological morphological analysis, can solve the problem of not finding patents, and achieve the effect of improving the accuracy rate
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Embodiment 1
[0055] According to a kind of simple automatic AV analysis method based on neural network provided by the present invention, as Figure 1-Figure 2 shown, including:
[0056] Step S1: collecting multiple micrographs of multiple samples;
[0057] Step S2: According to the scoring standard, mark the data of the four modules respectively;
[0058] Step S3: training according to modules, one model for each module;
[0059] Step S4: Each image enters four modules for identification respectively;
[0060] Step S5: The recognition results of each model are counted independently to obtain a single module scoring result;
[0061] Step S6: Accumulate the results of each module as the analysis result of the entire sample.
[0062] Specifically, in the step S1:
[0063] The collection uses an automatic biological microscope, 100 times oil lens, and collects 80 to 100 fields of view for each sample as the basis for judgment.
[0064] Specifically, in the step S2:
[0065] Modules wer...
Embodiment 2
[0076] Embodiment 2 is a preferred example of Embodiment 1 to describe the present invention more specifically.
[0077] Those skilled in the art can understand a simple neural network-based automatic AV analysis method provided by the present invention as a specific implementation of the simple neural network-based automatic AV analysis method, that is, the simple neural network-based automatic AV analysis method The AV analysis method can be realized by executing the steps of the simple neural network-based automatic AV analysis method.
[0078] A kind of simple automatic AV analysis system based on neural network provided by the present invention comprises:
[0079] Module M1: Collect multiple micrographs of multiple samples;
[0080] Module M2: according to the scoring standard, mark the data of the four modules respectively;
[0081] Module M3: training according to the modules, one model for each module;
[0082] Module M4: each picture enters four modules for identif...
Embodiment 3
[0099] Embodiment 3 is a preferred example of Embodiment 1 to describe the present invention more specifically.
[0100] The present invention uses a neural network to perform multi-angle identification and classification on multiple micrographs collected from a glass slide, modular scoring, and simulates manual scoring ideas to comprehensively obtain the evaluation results of aerobic vaginitis.
[0101] Manual scoring is performed according to the method in the attached table. Simulating manual means dividing the 4 goals of manual evaluation into 4 modules. After each module is evaluated separately, it is then accumulated to obtain the final result. Each module will have three scores from 0 to 3, and the proportion of the scores of each module will be counted. The score with the highest proportion will be the score of the entire module. The scores of the four modules will be added together to form the final overall AV score.
[0102] The collection uses our company's automati...
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