Semantic Features Prediction for Pulmonary Nodule Diagnosis Based on Online Streaming Feature Selection

Abstract

Early diagnosis significantly improves the survival rate in lung carcinoma patients. This study attempts to construct a predictive network between the computational features and semantic features of pulmonary nodules using online feature selection and causal structure learning. In this paper, we exploit the causal discovery based on the streaming feature algorithm and causal discovery with symmetrical uncertainty based on the streaming feature algorithm. Different from the traditional learning methods that usually obtain all computational features in advance and then select the optimal subset of features from the computational features, the proposed approach integrates online streaming feature selection with causal structure learning. The critical challenges in this integration include: 1) the dynamic selection of computational features and 2) how to evaluate the feature subsets and implement causal structure learning. In addition, considering that building a causal structure network is a time-consuming process, we improve the process by using support vector machines based on the streaming feature algorithm. The experimental results show that our proposed algorithms improve on other traditional feature selection algorithms and ensemble learning algorithm without feature selection with regard to learning accuracy.

A computed tomography signs quantization analysis method for pulmonary nodules malignancy grading

“…Moreover, since the Ref. 33 only gathers radiomics features in feature extraction, its accuracy is not as good as that of the method proposed by this article. Therefore, the feature set construction method based on CNN and radiomics features can reflect information in CT images in a more comprehensive way.…”

“…Reference 32 proposes a multilevel transfer learning method to assess the classification performance of pulmonary nodules, which extract features construct a neural network of each CT semantics, and input features to first‐order transfer network to explore which attribute can support cancer diagnosis. Reference 33 obtains feature sets of seven CT signs and malignancy degrees by extracting and selecting radiomics features and then feed them into classifiers to get lung nodule malignancy level. In Ref.…”

Improved meta‐heuristic algorithm for selecting optimal features: A big data classification model

“…The main aim of this paper is to reduce the correlation between data features when choosing the best features. The objective function is defined in Equation (19). Then, in Equation (20), the correlation between two data features Fea 1 and Fea 2 is represented, where i denoting the set of data features.…”

“…The feature extraction is the initial stage of preprocessing as it helps in extracting the data features. [15][16][17][18][19] The FS assists in deducing the dimensionality of the data features. The FS is also referred as subset selection, which makes use of the machine learning techniques to select the ideal data applicable for the learning algorithm.…”

“…The dimensionality reduction technique includes both feature extraction and selection. The feature extraction is the initial stage of preprocessing as it helps in extracting the data features 15–19 . The FS assists in deducing the dimensionality of the data features.…”