Hybrid Classification of Diffuse Liver Diseases in Ultrasound Images Using Deep Convolutional Neural Networks

Abstract Despite the fact that liver biopsy is considered to be the gold standard for detecting diffuse liver diseases, it is an invasive method with numerous side effects. Diffuse liver diagnosis using ultrasound imaging may be influenced by Physician subjectivity. Therefore, an accurate classification of liver diseases remains a notable demand. In this study, to categorize the liver status, a novel deep classifier, comprised of pre-trained deep convolutional neural networks (CNNs) is proposed. Several networks, namely ResNeXt, ResNet18, ResNet34, ResNet50, and AlexNet which concatenated with fully connected networks (FCNs) are used. Extracted deep features using transfer learning can provide sufficient classification information. An FCN can then put images into different states of the disease, namely normal liver, liver hepatitis, and cirrhosis. Two-class (normal/cirrhosis, normal/hepatitis, and cirrhosis/hepatitis) and three-class (normal/cirrhosis/hepatitis) classifiers were trained to distinguish these liver images. Since two-class classifiers showed better performance compared to the three-class classifiers, a hybrid classifier is proposed so as to integrate the weighted probabilities of the classes obtained by means of each individual classifier. Then, a majority voting strategy is employed to select the class with a higher score. The experimental results show an accuracy of 86.4% using ResNet50 with a hybrid classifier for liver images which were classified into three classes. In the distinction between normal and cirrhosis liver as well as normal and hepatitis liver, the results demonstrate the sensitivity and specificity of the first group to be 90.9% and 86.4% and the latter group shows the sensitivity of 90.9%, and specificity of 81.8%.