Computer-aided assessment of liver fibrosis progression in patients with chronic hepatitis B: an exploratory research

Zhonghua yi xue za zhi
T T FuW P Wang

Abstract

Objective: To establish automatic liver fibrosis classification models by using traditional machine learning and deep learning methods and preliminaryly evaluate the efficiency. Methods: Gray scale ultrasound images and corresponding elastic images of 354 patients, 247 males and 107 females, mean age (54±12) years undergoing partial hepatectomy in Zhongshan Hospital of Fudan University from November 2014 to January 2016 were enrolled in this study. By using traditional machine learning and deep learning methods, an automatic classification model of liver fibrosis stages (S0 to S4) were established through feature extraction and classification of ultrasound image data sets and the accuracy in different classification categories of each model were calculated, by using liver biopsy as the reference standard. Results: Pathological examination showed 73 cases in pathological stage S0, 40 cases in S1, 49 cases in S2, 41 cases in S3, and 151 cases in S4. The traditional machine classification model based on support vector machine (SVM) classifier and sparse representation classifier and the deep learning classification model based on LeNet-5 neural network, their accuracy rates in the two categories (S0/S1/S2 and S3/S4) were 89.8%, 91...Continue Reading

Related Concepts

Related Feeds

Barrel cortex

Here is the latest research on barrel cortex, a region of somatosensory and motor corticies in the brain, which are used by animals that rely on whiskers for world exploration.