Intelligent classification of endoscopic images and detection method of irregular lesion areas
A lesion area and irregular technology, applied in the field of medical image intelligent processing, can solve the problems of manual feature difficulty and achieve the effect of improving application value and accuracy
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[0045] use figure 1 The method shown uses annotated endoscopic images to train a saliency detection network model to convergence, and this model is used for endoscopic image classification and irregular lesion area detection. For an endoscopic image to be tested, you can use figure 2 The method shown. details as follows:
[0046] The specific implementation method is:
[0047] (1) Preprocess the image, according to the value of the R channel in the RGB channel of the endoscopic image (range 0-255), use a smaller threshold (such as 30) to binarize the image, and remove the low value of the binarized image And record the location information of the reserved area as (x, y, H, W);
[0048] (2) Input the preprocessed image into the network model to obtain the saliency map M, the threshold T is set to 0.5, if there is M(i,j)>T in M, the endoscopic image is classified as a lesion, otherwise it is classified It is normal; if the endoscopic image is a lesion, the pixels that may have lesi...
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