Biopsy area prediction method, image recognition method, device and storage medium
An image recognition and area technology, applied in the field of communication, can solve the problems of missed detection, low accuracy and effectiveness of biopsy areas, etc., and achieve the effect of improving accuracy and effectiveness, and reducing the probability of missed detection
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Embodiment 1
[0065] This embodiment will be described from the perspective of a biopsy area prediction device. The biopsy area prediction device may be integrated in a network device, which may be a terminal or a server, where the terminal may include a tablet computer, a notebook computer or Personal computer (PC, Personal Computer), etc.
[0066] An embodiment of the present invention provides a method for predicting a biopsy area, including: collecting an image of a living body tissue to be detected, using a preset lesion area detection model to detect a lesion area on the image of a living body tissue, and if a lesion area is detected, using a preset The algorithm is used to preprocess the lesion area to obtain the area to be identified, and the preset lesion classification model is used to classify the area to be identified, and the predicted probability of the lesion corresponding to the area to be identified is obtained as the classification result, and the predicted probability of t...
Embodiment 2
[0112] According to the methods described in the previous embodiments, the following will take an example in which the biopsy area prediction device is specifically integrated in a network device for further detailed description.
[0113] (1) First, it is necessary to train the lesion area detection model and lesion classification model, which can be as follows:
[0114] (1) Training of lesion region detection model.
[0115] The network device collects multiple sample images of living tissue samples marked with diseased areas, and then trains the preset target detection model according to the sample images of living body tissue. For example, it can specifically determine the current The sample image of living body tissue that needs to be trained, and then input the current sample image of living body tissue that needs to be trained into the preset target detection model for detection to obtain the predicted lesion area, and the predicted lesion area and the marked lesion area...
Embodiment 3
[0153] On the basis of the above embodiments, the embodiments of the present invention also provide an image recognition method and device.
[0154] Wherein, the image recognition device can specifically be integrated in a network device, and the network device can be a terminal or a server, for example, see Figure 3a , the network device can collect images of living body tissues to be detected, for example, it can specifically receive images of living body tissues sent by some image acquisition devices, such as colposcopes or endoscopes (such as colposcope images or endoscope images, etc.), Then, classify the vital tissue image to obtain an image classification result; when the image classification result is a lesion, use a preset lesion area detection model to detect a lesion area on the vital tissue image, and if a lesion area is detected, then Use the preset algorithm to preprocess the lesion area, such as merging and resetting, to obtain the area to be identified, and th...
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