Liver Fibrosis Classification Based on Transfer Learning and FCNet for Ultrasound Images

Diagnostic ultrasound offers great improvements in diagnostic accuracy and robustness. However, it is difficult to make subjective and uniform diagnoses, because the quality of ultrasound images can be easily influenced by machine settings, the characteristics of ultrasonic waves, the interactions between ultrasound and body tissues, and other uncontrollable factors. In this paper, we propose a novel liver fibrosis classification method based on transfer learning (TL) using VGGNet and a deep classifier called fully connected network (FCNet). In case of insufficient samples, deep features extracted using TL strategy can provide sufficient classification information. These deep features are then sent to FCNet for the classification of different liver fibrosis statuses. With this framework, tests show that our deep features combined with the FCNet can provide suitable information to enable the construction of the most accurate prediction model when compared with other methods.