Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images.

ABSTRACT:

Purpose

The nonalcoholic fatty liver disease is the most common liver abnormality. Up to date, liver biopsy is the reference standard for direct liver steatosis quantification in hepatic tissue samples. In this paper we propose a neural network-based approach for nonalcoholic fatty liver disease assessment in ultrasound.

Methods

We used the Inception-ResNet-v2 deep convolutional neural network pre-trained on the ImageNet dataset to extract high-level features in liver B-mode ultrasound image sequences. The steatosis level of each liver was graded by wedge biopsy. The proposed approach was compared with the hepatorenal index technique and the gray-level co-occurrence matrix algorithm. After the feature extraction, we applied the support vector machine algorithm to classify im

SUBMITTER: Byra M 

PROVIDER: S-EPMC6223753 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature