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

Claim 0:
1. A method for classifying a medical image, the method comprising:
obtaining, by a device comprising a memory storing instructions and a processor in communication with the memory, a medical image data set;
performing, by the device, 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 comprising at least one of the following: hue-saturation-lightness analysis, definition analysis, texture analysis, or entropy value analysis, by:
performing, by the device, 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

classifying, by the device, 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:
performing the hue-saturation-lightness analysis comprises converting 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;
performing the definition analysis comprises:
calculating a value of a digital image matrix of the medical image,
performing convolution on the value of the digital image matrix and a 5×5 Gaussian convolution kernel to obtain a convolution value, and
calculating 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;

performing the texture analysis comprises extracting a grayscale edge of the medical image by using a Sobel verification operator; and
performing the entropy value analysis comprises calculating 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.