Patent ID: 11954852
Assignee: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
Field: Medical technology (Instruments)
Classification: CPC G | IPC G

Claim 5:
6. An apparatus for classifying a medical image, the apparatus comprising:
a memory storing instructions; and
a processor in communication with the memory, wherein, when the processor executes the instructions, the processor is configured to cause the apparatus to:
obtain a medical image data set,
perform quality analysis on the medical image data set, to extract feature information of a medical image in the medical image data set, the quality analysis comprises at least one of the following: hue-saturation-lightness analysis, definition analysis, texture analysis, or entropy value analysis, by:
performing at least one of the following: the hue-saturation-lightness analysis, the definition analysis, the texture analysis, or the entropy value analysis on the medical image data set, to extract the feature information of the medical image, wherein the feature information comprises at least one of the following: hue feature information, saturation feature information, lightness feature information, a definition index, a grayscale edge, or an entropy value, and

classify the medical image data set based on the feature information and by using a pre-trained deep learning network for performing anomaly detection and classification, to obtain a classification result,

wherein:
when the processor is configured to cause the apparatus to perform the hue-saturation-lightness analysis, the processor is configured to cause the apparatus to convert values of a red coordinate, a green coordinate, and a blue coordinate of the medical image from an RGB space to an HSV space, to obtain hue feature information, saturation feature information, and lightness feature information;
when the processor is configured to cause the apparatus to perform the definition analysis, the processor is configured to cause the apparatus to:
calculate a value of a digital image matrix of the medical image,
perform convolution on the value of the digital image matrix and a 5×5 Gaussian convolution kernel to obtain a convolution value, and
calculate a definition index of the medical image based on a minimum mean square error of the value of the digital image matrix and the convolution value;

when the processor is configured to cause the apparatus to perform the texture analysis, the processor is configured to cause the apparatus to extract a grayscale edge of the medical image by using a Sobel verification operator; and
when the processor is configured to cause the apparatus to perform the entropy value analysis, the processor is configured to cause the apparatus to calculate an entropy value according to a length of the medical image, a width of the medical image, and a quantity of times and a probability that a grayscale value of a center pixel in a sliding window and an average of grayscale values other than that of the center pixel in the sliding window occur in the medical image.