Automatic Classification of Hepatic Cystic Echinococcosis Using Ultrasound Images and Deep Learning

Background Hepatic cystic echinococcosis is the main form of hepatic echinococcosis, which is a life-threatening liver disease caused by parasites that requires a precise diagnosis and proper treatment. Objective This study focuses on the automatic classification system of five different subtypes of hepatic cystic echinococcosis based on ultrasound images and deep learning algorithms. Methods Three popular deep convolutional neural networks (VGG19, Inception-v3, and ResNet18) with and without pretrained weights were selected to test their performance on the classification task, and the experiments were followed by a 5-fold cross-validation process. Results A total of 1820 abdominal ultrasound images covering five subtypes of hepatic cystic echinococcosis from 967 patients were used in the study. The classification accuracy for the models with pretrained weights (fine-tuning) ranged from 88.2 to 90.6%. The best accuracy of 90.6% was obtained by VGG19. For comparison, the models without pretrained weights (from scratch) achieved a lower accuracy, ranging from 69.4 to 75.1%. Conclusion Deep convolutional neural networks with pretrained weights are capable of recognizing different subtypes of hepatic cystic echinococcosis from ultrasound images, which are expected to be applied in the computer-aided diagnosis systems in future work.