Virus screening and epidemic prevention system and method based on neural network
A neural network and neural network model technology, applied in the field of virus screening and epidemic prevention systems based on neural networks, can solve the problems of lack of virus detection data storage and analysis, low timeliness, etc., achieve low cost, eliminate tension and anxiety, Addressing the effects of unclear infection rates
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Embodiment 1
[0043] figure 1 Show a kind of virus screening and anti-epidemic method based on neural network of exemplary embodiment of the present invention, comprise: Step 1, obtain the specific virus colloidal gold antibody detection image data (also called virus reagent detection image);
[0044] Specifically, we set a front-end image acquisition module above the corresponding colloidal gold reagent test strip. This setting is similar to the existing blood glucose meter detection equipment, and there is an acquisition device inside, so that the control chip can directly receive the user's blood glucose. Test the results and analyze the results. The virus reagent detection image acquisition module includes: a macro camera, a main control chip, a bluetooth module, and a battery; The chip is respectively connected with the macro camera and the Bluetooth module, and is used to control the macro camera to collect the user's specific virus colloid gold antibody detection image data based o...
Embodiment 2
[0060] In a further embodiment of the present invention, we have conducted targeted research on the virus detection model for the current difficult novel coronavirus. The detection kit has three detection lines for the new coronary pneumonia screening kit, where the C line is the control line, T1, The two lines of T2 respectively represent the detection lines of IGM and IgG antibodies. The detection of colloidal gold is a dynamic process, and the detection is invalid beyond a certain period of time. The specific model identification process is as follows: firstly, the color of the control line c is relatively obvious, and the matching algorithm based on edge features is used to locate the c line. If the control line C cannot be found, the detection is invalid. The positions of the T1 and T2 lines can be inferred from the center position of the c-line. By judging the average color difference between the control line C, the test lines T1, T2 and the background of the reagent st...
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