基于深度DenseNet网络的肝包虫病超声影像诊断方法

Ultrasonic Image Diagnosis of Hepatic Echinococcosis Based on Deep DenseNet Network

肝包虫病是一种严重的区域性寄生虫病,病变诊断和分类主要依靠临床医生对超声图像的主观判断,在医疗条件薄弱地区,该疾病的筛查和诊断极易误判错判。为了提高肝包虫病的诊断效率和诊断精度,本文结合深度学习算法,将深度DenseNet网络应用于肝包虫病图像分类问题中,利用深度卷积神经网络强大的特征提取能力构建肝包虫分类模型;此外,为了能够提供更可靠的图像输入信息,使用基于ROI的预处理方法,提取原始图像病灶ROI区域;最后,在构建的超声影像数据集上进行模型评估和验证,准确率可以达到93%,并使用梯度加权类激活映射图进行可视化分析,表明模型具有较强的鲁棒性和较好的分类效果。

Hepatic echinococcosis is a serious regional parasitic disease.The diagnosis and classification of lesions mainly rely on the subjective judgment of the clinician on ultrasound images.In areas with weak medical conditions,the screening and diagno⁃sis of the disease can easily be misjudged.In order to improve the diagnosis efficiency and accuracy of liver hydatid disease,this paper combines deep learning algorithms to apply the deep DenseNet network to the image classification problem of liver hydatid disease,and uses the powerful feature extraction capabilities of deep convolutional neural networks to construct liver hydatid classification model.In addition,in order to be able to provide more reliable image input information,the ROI-based preprocess⁃ing method is used to extract the lesion ROI area of the original image.Finally,the model is evaluated and verified on the con⁃structed data set,and the accuracy can reach 93%,and by using gradient weighted class activation map for visual analysis,it showes that the model has strong robustness and better classification effect.