Learning with distribution of optimized features for recognizing common CT imaging signs of lung diseases

Ma, Ling; Liu, Xiabi; Fei, Baowei

doi:10.1088/1361-6560/62/2/612

Cited by 3 publications

(4 citation statements)

References 47 publications

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“…Two main stages are incorporated: (1) training procedure and (2) multi-level similarity retrieval procedure. In the training procedure, the visual representation of the CISLs is extracted, the attribute representation is abstracted by performing the auto-encoder (AE) neural networks, and the semantic representation is achieved by learning with distribution of optimized features (DOF) [45]. According to the multi-level representation of CISLs, the similarities at multiple levels are computed and combined in a weighted sum form as the final similarity.…”

Section: Methodsmentioning

confidence: 99%

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A multi-level similarity measure for the retrieval of the common CT imaging signs of lung diseases

Liu

Fei

2020

Med Biol Eng Comput

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The common CT imaging signs of lung diseases (CISLs) which frequently appear in lung CT images are widely used in the diagnosis of lung diseases. Computer-aided diagnosis (CAD) based on the CISLs can improve radiologists' performance in the diagnosis of lung diseases. Since similarity measure is important for CAD, we propose a multi-level method to measure the similarity between the CISLs. The CISLs are characterized in the low-level visual scale, mid-level attribute scale, and high-level semantic scale, for a rich representation. The similarity at multiple levels is calculated and combined in a weighted sum form as the final similarity. The proposed multi-level similarity method is capable of computing the level-specific similarity and optimal cross-level complementary similarity. The effectiveness of the proposed similarity measure method is evaluated on a dataset of 511 lung CT images from clinical patients for CISLs retrieval. It can achieve about 80% precision and take only 3.6 ms for the retrieval process. The extensive comparative evaluations on the same datasets are conducted to validate the advantages on retrieval performance of our multi-level similarity measure over the single-level measure and the two-level similarity methods. The proposed method can have wide applications in radiology and decision support.

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Section: Methodsmentioning

confidence: 99%

“…We extract the four different types of features to better character the ROI. Since these features may contain complementary or irrelevant information, we adopt a feature selection method [45] to select the more compact and discriminative features for the description of ROIs at visual level.…”

Section: Methodsmentioning

confidence: 99%

A multi-level similarity measure for the retrieval of the common CT imaging signs of lung diseases

Liu

Fei

2020

Med Biol Eng Comput

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“…Sun [25] and Liu [24] refined the traditional genetic optimization feature selection algorithm to improve the classification performance of benign and malignant lung nodules by selecting the optimal subset of features for identifying CISs from the numerous graphical feature sets of lung nodules. Ma [26] proposed a feature selection method based on minimum spanning trees, which considered the relationship between different features on the basis of the traditional method and captured the relationship between the discriminative power of different features, so as to select the most important features for sign recognition. The feature selection technique was tested on 511 actual CT images with a classification accuracy of 91.96%.…”

Section: Feature Classificationmentioning

confidence: 99%

“…Ref. [26] trained k AdaBoost classifiers to recognize nine common indicators, such as spiculation, cavity, pleural indentation, and lobulation, acquired k classification decisions, and ultimately utilized voting to achieve the final decision. This strategy enhanced the classification accuracy by 15.17% compared to the soft classification method [27].…”

Section: Feature Classificationmentioning

confidence: 99%

The Progress on Lung Computed Tomography Imaging Signs: A Review

Xiao

Jiang

et al. 2022

Applied Sciences

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Lung cancer is the highest-mortality cancer with the largest number of patients in the world. Early screening and diagnosis of lung cancer by CT imaging is of great significance to improve the cure rate of lung cancer. CT signs mean the information of comprehensive manifestations of diseases at different pathological stages and levels. Automatic analysis of CT images outputs the locations and sizes of lesion regions which can help radiologists to make a credible diagnosis and effectively improve the speed and accuracy of clinical diagnosis. In this paper, we first review the domestic and foreign research progress of lung CT signs, summarize a generic structure for expressing the implementation process of existing methods, and systematically describe the signs research based on the traditional machine learning method and deep learning method. Furthermore, we provide a systematic summary and comparative analysis of the existing methods. Finally, we point out the challenges ahead and discuss the directions for improvement of future work, providing reference for scholars in related fields.

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A novel fusion algorithm for benign-malignant lung nodule classification on CT images

Ma,

Wan,

Hao

et al. 2023

BMC Pulm Med

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The accurate recognition of malignant lung nodules on CT images is critical in lung cancer screening, which can offer patients the best chance of cure and significant reductions in mortality from lung cancer. Convolutional Neural Network (CNN) has been proven as a powerful method in medical image analysis. Radiomics which is believed to be of interest based on expert opinion can describe high-throughput extraction from CT images. Graph Convolutional Network explores the global context and makes the inference on both graph node features and relational structures. In this paper, we propose a novel fusion algorithm, RGD, for benign-malignant lung nodule classification by incorporating Radiomics study and Graph learning into the multiple Deep CNNs to form a more complete and distinctive feature representation, and ensemble the predictions for robust decision-making. The proposed method was conducted on the publicly available LIDC-IDRI dataset in a 10-fold cross-validation experiment and it obtained an average accuracy of 93.25%, a sensitivity of 89.22%, a specificity of 95.82%, precision of 92.46%, F1 Score of 0.9114 and AUC of 0.9629. Experimental results illustrate that the RGD model achieves superior performance compared with the state-of-the-art methods. Moreover, the effectiveness of the fusion strategy has been confirmed by extensive ablation studies. In the future, the proposed model which performs well on the pulmonary nodule classification on CT images will be applied to increase confidence in the clinical diagnosis of lung cancer.

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Learning with distribution of optimized features for recognizing common CT imaging signs of lung diseases

Cited by 3 publications

References 47 publications

A multi-level similarity measure for the retrieval of the common CT imaging signs of lung diseases

A multi-level similarity measure for the retrieval of the common CT imaging signs of lung diseases

The Progress on Lung Computed Tomography Imaging Signs: A Review

A novel fusion algorithm for benign-malignant lung nodule classification on CT images

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