Ultrasonic Image Diagnosis of Liver and Spleen Injury Based on a Double-Channel Convolutional Neural Network

Maorui He,Rui Zhang,Yang Zeng,Yansong Tan,Shuni Liu,Wenqing Wu

doi:10.1155/2021/2827011

Maorui He, Rui Zhang + Show 4 more

Open Access

https://doi.org/10.1155/2021/2827011

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Abstract

Automatic and accurate diagnosis of liver and spleen injury in ultrasonic images is of great significance for the development of automatic clinical diagnosis. In order to realize more accurate ultrasonic image diagnosis of liver and spleen injury, an algorithm of ultrasonic image classification diagnosis of liver and spleen injury based on double-channel convolutional neural network was proposed. Firstly, the anisotropic diffusion denoising model is used to realize data preprocessing of ultrasonic images of the liver and spleen to improve the image quality of ultrasonic images. Secondly, the external edge of the lesion location was detected to obtain the characteristics of the external edge. Then, the rotation invariant local binary mode feature of the extracted image is taken as the inner texture feature of the image. Finally, the external edge feature and internal texture feature are used as two input channels of the convolutional neural network, respectively, to classify and identify ultrasonic images of liver and spleen injury. The experimental results show that the proposed method diagnoses liver and spleen injury more accurately.

Highlights

Ultrasound imaging technology is widely used in clinical medicine due to its nonradiation damage and noninvasive features, such as the observation and diagnosis of the liver, gallbladder, spleen, kidney, and other vital organs of the human body
In order to achieve more accurate ultrasound image diagnosis of liver and spleen injury, this paper proposes a classification and diagnosis algorithm for ultrasound images of liver and spleen injury based on a double-channel convolutional neural network
External edge features and internal texture features are taken as input information of the dual channel convolutional neural network, respectively

Summary

Introduction

Ultrasound imaging technology is widely used in clinical medicine due to its nonradiation damage and noninvasive features, such as the observation and diagnosis of the liver, gallbladder, spleen, kidney, and other vital organs of the human body. In order to identify the damage of liver and spleen ultrasound images, CNN will be used as an identification network in this paper. In order to achieve more accurate ultrasound image diagnosis of liver and spleen injury, this paper proposes a classification and diagnosis algorithm for ultrasound images of liver and spleen injury based on a double-channel convolutional neural network. An algorithm based on dual channel convolution neural network is designed to recognize and diagnose ultrasound images of liver and spleen injury. The clinical medical ultrasound image is used as a data set and compared with other recognition algorithms to further verify the effectiveness and accuracy of this algorithm

The Basic Theory

Algorithm Implementation

Experimental Results and Analysis

Conclusion and Future Work