Automatic Thyroid Ultrasound Image Classification Using Feature Fusion Network

Xiaohui Zhao,Xueqin Shen,Wenbo Wan,Ruoxiu Xiao,Xiaohui Du,Yuanyuan Lu,Shidong Hu,Junlai Li

doi:10.1109/access.2022.3156096

Abstract

Nowadays, diagnosis of thyroid nodules is mainly based on clinical methods, which requires a lot of manpower and medical resources. Therefore, this work proposes an automated thyroid ultrasound nodule diagnosis method that combines convolutional neural networks and image texture features. The main steps include: Firstly, ultrasound thyroid nodule dataset is established by collecting positive and negative samples, standardizing of images and segmentation of nodule area. Secondly, through texture features extraction, feature selection and data dimensionality reduction, texture features model is obtained; Thirdly, by transfer learning, deep neural network is used to obtain feature model of the nodule in images; Then, texture features model and convolutional neural network feature model are combined to form a new nodule feature model called Feature Fusion Network; Finally, Feature Fusion Network is applied to train and improve performance than single network, and a deep neural network diagnosis model that can adapt to the characteristics of thyroid nodules is built. In order to test this method, 1874 groups of clinical ultrasound thyroid nodules are collected. Harmonic average F-score based on Precision and Recall is used as an evaluation indicator. Experimental results show that Feature Fusion Network can distinguish between benign and malignant thyroid nodules with an F-score of 92.52%. Compared with traditional machine learning methods and convolutional neural networks, performance of this work is better.

Highlights

Based on patient's pathology test, texture information of nodules is obtained by feature engineering, and features selection is performed based on chi-square correlation test of relationship between feature variable and nodules to eliminate influence of irrelevant variables; transfer learning is applied to establish a ResNet to achieve feature extraction and image texture features fusion, so that the performance of network is further improved
Based on the nodules area, feature engineering is applied to obtain texture features of nodules, and feature dimensionality reduction is realized through the correlation between features and nodules and the relationship between features
A deep neural network model is established, and texture features from the previous step are merged to achieve the goal of further improving network performance

Summary

Introduction

With the increase of people’s life pressure and breakthroughs in medical testing technology, prevalence of thyroid nodules has increased year by year in the world, becoming one of the most important diseases threatening human health [1]. Early diagnosis of thyroid nodules is very important [2]. The diagnostic methods of thyroid nodules mainly include ultrasound examination, CT examination, aspiration biopsy and pathological examination. Needle biopsy and pathological examination are more commonly used and reliable methods, but these two methods are very traumatic to thyroid tissue. Ultrasonography is currently the common imaging method for diagnosing thyroid diseases. It has the advantages of simplicity, good reproducibility, noninvasive, fast and low price. The ability to accurately and quickly identify and diagnose the pathology of ultrasound thyroid nodules has become an increasingly urgent need

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