Ultrasonic image classification device

Abstract

The embodiment of the invention discloses an ultrasonic image classification device, and the device comprises a conversion unit which carries out the format conversion of an obtained ultrasonic image,and obtains a target ultrasonic image; and a residual error processing unit used for carrying out residual error processing on the target ultrasonic image to obtain a plurality of sub-images. According to the invention, residual error processing is carried out on the image, and the ultrasonic image can be segmented into a plurality of sub-images, so that the ultrasonic image can be divided into smaller image units for processing, and the accuracy of image analysis is effectively improved. Compared with the traditional convolution analysis, the residual error processing mode can reduce the data processing amount, so that the image classification processing efficiency is improved. And the determination unit analyzes the plurality of sub-images by using the trained capsule network to determine the image category to which the ultrasonic image belongs. The capsule network can obtain the correlation between the ultrasonic image and each image category by performing clustering analysis on the sub-images, so that the image category to which the ultrasonic image belongs can be evaluated more accurately.

Description

technical field [0001] The invention relates to the technical field of ultrasonic image processing, in particular to an ultrasonic image classification device. Background technique [0002] Papillary thyroid carcinoma is the most common type of cancer. The manifestations of papillary thyroid carcinoma can be divided into irregular shape, unclear border, uneven echo, calcification and other symptoms. Ultrasonography is the imaging study of choice. Medical imaging data is complex and diverse, and has high requirements for doctors' work. In the case where a large number of ultrasound images need to be diagnosed, the physician's diagnostic efficiency tends to decrease as the number of ultrasound images increases. Since the doctor's fatigue will increase the rate of misjudgment, the results combined with artificial intelligence and other related technologies can be pre-processed and sent to the doctor for judgment, which can greatly reduce the workload of the doctor and improve...

AI Technical Summary

Problems solved by technology

Due to the high complexity of medical images, ultrasound images have a series of problems such as cell overlap, blurred edges, and blood vessel interference. Only using convolutional neural networks as classifiers often results in a large number of false positives. little help to physicians in the process

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