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2023 | OriginalPaper | Buchkapitel

Deep Learning and XGBoost Based Prediction Algorithm for Esophageal Varices

verfasst von : Xinyi Chen, Jiande Sun, Zhishun Wang, Yanling Fan, Jianping Qiao

Erschienen in: Signal and Information Processing, Networking and Computers

Verlag: Springer Nature Singapore

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Abstract

The deep convolutional neural network has been extensively applied for clinical computer-aided diagnosis. In this study, we combine deep learning feature extraction and eXtreme gradient boosting (XGBoost) classifier for predicting the risk of esophageal varices (EV). First, the quantitative deep learning features and radiomics features of the regions of interest which includes spleen, liver and esophageal are extracted and concatenated. Then, XGBoost and the Least Absolute Shrinkage and Selection Operator (LASSO) are applied for the optimal predictive features selection and prediction of EV risk. XGBoost is used to assess the significance of the extracted features and LASSO is used to select the distinctive features. Finally, random forest, XGBoost and support vector machine classification methods are applied for predicting the low-risk and high-risk of esophageal varices. We collected computed tomography images of cirrhotic patients in two hospitals as the independent training and validation sets. Experimental results show that the features of esophageal are more distinctive than that of other organs. Moreover, the combination of deep learning and radiomics features based on XGBoost algorithm has outperforming classification performance in predicting the severity of EV disease compared to existing approaches.

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Metadaten
Titel
Deep Learning and XGBoost Based Prediction Algorithm for Esophageal Varices
verfasst von
Xinyi Chen
Jiande Sun
Zhishun Wang
Yanling Fan
Jianping Qiao
Copyright-Jahr
2023
Verlag
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-19-3387-5_134

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