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

Abstract

In order to improve the accuracy of pulmonary nodules malignancy grading, we propose a method to implement quantitative analysis for lung nodules using computed tomography (CT) signs. Firstly, we construct feature sets of CT signs by combing the radiomics features with the higher‐order features extracted from a convolutional neural network. Secondly, on the basis of the mixed feature set, an evolutionary ensemble learning mechanism is used to generate a classifier to get the quantitative scores for seven lung nodule CT signs. Finally, the scores of seven CT signs are input into a multiclassifier optimized by the differential evolution algorithm to acquire the grade of malignancy. In the experimental study, 2000 lung nodule samples from the LIDC‐IDRI dataset were used to train and test the evolutionary ensemble learner and malignancy classifier. The results show that the recognition accuracy of seven CT signs can reach more than 0.964. Comparison with many typical algorithms, the proposed method not only gets higher accuracy in pulmonary nodules malignancy grading but also can make the result more interpretable.

Gray level fuzzy deep neural networks for enhancing performance in lung disease detection: A comparative study with fuzzy logic methods

“…It employed extreme learning machine and integrated local feature descriptors. 12 Another malignancy grading technique for pulmonary nodules using CT signs quantization analysis was investigated. 10 Atanassov's intuitionistic fuzzy sets introduced a segmentation technique.…”

“…A differentiation method was presented for detecting pulmonary TB in chest radiographs. It employed extreme learning machine and integrated local feature descriptors 12 . Another malignancy grading technique for pulmonary nodules using CT signs quantization analysis was investigated 10 …”

“…To improve the efficiency and accuracy of CNN, many researchers have developed various hybrid mechanisms. [10][11][12][13] To address the challenges of lesion segmentation, several techniques were used to optimize the segmentation algorithm. However, due to the varying depths of infection, lesion segmentation remains a challenging task.…”

“…In addition, another unsupervised technique called the convolutional neural network (CNN) segmentation tool, has emerged in recent years. To improve the efficiency and accuracy of CNN, many researchers have developed various hybrid mechanisms 10–13 …”