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

Zhengye Wang; Miao Wu; Qian Liu; Xiaorong Wang; Chuanbo Yan; Tao Song

doi:10.1016/j.ultrasmedbio.2024.03.018

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

Ultrasound Med Biol. 2024 Apr 27:S0301-5629(24)00151-0. doi: 10.1016/j.ultrasmedbio.2024.03.018. Online ahead of print.

Authors

Zhengye Wang¹, Miao Wu², Qian Liu³, Xiaorong Wang⁴, Chuanbo Yan², Tao Song⁵

Affiliations

¹ Center for Disease Control and Prevention, Xinjiang Production and Construction Corps, Urumqi, China; Ultrasound Department, State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Disease in Central Asia, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
² College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China.
³ Basic Medical College, Xinjiang Medical University, Urumqi, China.
⁴ Ultrasound Department, State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Disease in Central Asia, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
⁵ Ultrasound Department, State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Disease in Central Asia, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China. Electronic address: doctorsongtao@163.com.

PMID: 38679514
DOI: 10.1016/j.ultrasmedbio.2024.03.018

Abstract

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.

Keywords: Depth features; Hepatic cystic echinococcosis; Multiclassification; Multiple kernel learning; Texture features; Ultrasound images.