A computer implemented method for processing a medical image, comprising: obtaining at least one image sample, wherein the at least one image sample is an image obtained by taking a medical image of at least one animal body; marking a tumor region (301, 601, 602) in the at least one image sample and an appearance feature thereof, wherein the appearance feature represents an appearance of a first tumor corresponding to the tumor region (301, 601, 602); training an image recognition model according to the at least one image sample, the tumor region (301, 601, 602) from the at least one image samples and the appearance feature of the first tumor, wherein the image recognition model after trained with actual results of the training samples, which are a first type, predicts a second tumor in an image to be evaluated is the first type, and the first type is related to the tumor region (301, 601, 602) and the appearance feature of the first tumor; characterized in that the method for processing a medical image further comprises: respectively inputting a first image to be evaluated and a second image to be evaluated to the trained image recognition model to obtain a first prediction result and a second prediction result, wherein the first image to be evaluated is photographed at an angle relative to one of the at least one animal body, the first image to be evaluated input to the image recognition model is marked with a tumor region (301, 601, 602) of the second tumor, and the second image to be evaluated is photographed at another angle relative to the same animal body; and generating another prediction result of the same animal body according to a statistical result of the first prediction result and the second prediction result. obtaining at least one image sample, wherein the at least one image sample is an image obtained by taking a medical image of at least one animal body; marking a tumor region (301, 601, 602) in the at least one image sample and an appearance feature thereof, wherein the appearance feature represents an appearance of a first tumor corresponding to the tumor region (301, 601, 602); training an image recognition model according to the at least one image sample, the tumor region (301, 601, 602) from the at least one image samples and the appearance feature of the first tumor, wherein the image recognition model after trained with actual results of the training samples, which are a first type, predicts a second tumor in an image to be evaluated is the first type, and the first type is related to the tumor region (301, 601, 602) and the appearance feature of the first tumor; characterized in that the method for processing a medical image further comprises: respectively inputting a first image to be evaluated and a second image to be evaluated to the trained image recognition model to obtain a first prediction result and a second prediction result, wherein the first image to be evaluated is photographed at an angle relative to one of the at least one animal body, the first image to be evaluated input to the image recognition model is marked with a tumor region (301, 601, 602) of the second tumor, and the second image to be evaluated is photographed at another angle relative to the same animal body; and generating another prediction result of the same animal body according to a statistical result of the first prediction result and the second prediction result. respectively inputting a first image to be evaluated and a second image to be evaluated to the trained image recognition model to obtain a first prediction result and a second prediction result, wherein the first image to be evaluated is photographed at an angle relative to one of the at least one animal body, the first image to be evaluated input to the image recognition model is marked with a tumor region (301, 601, 602) of the second tumor, and the second image to be evaluated is photographed at another angle relative to the same animal body; and generating another prediction result of the same animal body according to a statistical result of the first prediction result and the second prediction result.