Convolutional neural network classification of ultrasound images by liver fibrosis stages based on echo-envelope statistics

Introduction: Assessing the stage of liver fibrosis during the diagnosis and follow-up of patients with diffuse liver disease is crucial. The tissue structure in the fibrotic liver is reflected in the texture and contrast of an ultrasound image, with the pixel brightness indicating the intensity of the echo envelope. Therefore, the progression of liver fibrosis can be evaluated non-invasively by analyzing ultrasound images. Methods: A convolutional-neural-network (CNN) classification of ultrasound images was applied to estimate liver fibrosis. In this study, the colorization of the ultrasound images using echo-envelope statistics that correspond to the features of the images is proposed to improve the accuracy of CNN classification. In the proposed method, the ultrasound image is modulated by the 3rd- and 4th-order moments of pixel brightness. The two modulated images and the original image were then synthesized into a color image of RGB representation. Results and Discussion: The colorized ultrasound images were classified via transfer learning of VGG-16 to evaluate the effect of colorization. Of the 80 ultrasound images with liver fibrosis stages F1–F4, 38 images were accurately classified by the CNN using the original ultrasound images, whereas 47 images were classified by the proposed method.