Multiclassification of Hepatic Cystic Echinococcosis by Using Multiple Kernel Learning Framework and Ultrasound Images.

UNLABELLED: To properly treat and care for hepatic cystic echinococcosis (HCE), it is essential to make an accurate diagnosis before treatment.

OBJECTIVE: The objective of this study was to assess the diagnostic accuracy of computer-aided diagnosis techniques in classifying HCE ultrasound images into five subtypes.

METHODS: A total of 1820 HCE ultrasound images collected from 967 patients were included in the study. A multi-kernel learning method was developed to learn the texture and depth features of the ultrasound images. Combined kernel functions were built-in Support Vector Machine (MK-SVM) for the classification work. The experimental results were evaluated using five-fold cross-validation. Finally, our approach was compared with three other machine learning algorithms: the decision tree classifier, random forest, and gradient boosting decision tree.

RESULTS: Among all the methods used in the study, the MK-SVM achieved the highest accuracy of 96.6% on the fused feature set.

CONCLUSION: The multi-kernel learning method effectively learns different image features from ultrasound images by utilizing various kernels. The MK-SVM method, which combines the learning of texture features and depth features separately, has significant application value in HCE classification tasks.