Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction

Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the Breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI-RADS features are then extracted from the shape, margin, and density of each mass, together with the mass size and the patient’s age, as mentioned in BI-RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI-RADS features. Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database for screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI-RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI-RADS categories.

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Feature Selection Based on Gaussian Mixture Model Clustering for the Classification of Pulmonary Nodules Based on Computed Tomography

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.3008 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1033-1039

Author(s):

Huihong Duan ◽

Xu Wang ◽

Xingyi He ◽

Yonggang He ◽

Litao Song ◽

...

Keyword(s):

Computed Tomography ◽

Feature Selection ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Pulmonary Nodules ◽

Gaussian Mixture ◽

Local Optimum ◽

Computer Aided ◽

Aided Diagnosis

Background: In the pulmonary nodules computer aided diagnosis systems (CAD), feature selection plays an important role in reducing the false positive rate and improving the system accuracy. To solve the problem of feature selection techniques by which the diversity of features was damaged in the process of distinguishing malignant pulmonary nodules from benign pulmonary nodules, this study developed a novel feature selection algorithm for improving the accuracy of traditional computer-aided differential diagnosis for benign and malignant classification of pulmonary nodules. Method: Firstly, we divided the extracted features of nodules into several groups by using Gaussian mixture model (GMM). Secondly, we applied Relief and sequential forward selection (SFS) algorithm to find local optimum features dataset for each group. Afterwards, we used the optimumpath forest (OPF) classifier with the found features dataset to obtain the classification results. Finally, the local optimum features dataset with the highest area under curve AUC in all groups were added into the final selected set. Results: According to collected pulmonary nodules on computed tomography (CT) scans, tested with two set of samples, we achieved an average accuracy of 89.5%, sensitivity of 87.1% and specificity of 90.9% on the first set of samples, and 90.1%, 88.7% and 92.1% on the second set of samples. The areas under the receiver operating characteristic (ROC) curves based on these two sample sets were 95.2%, and 96.3% respectively. Conclusions: This study shows that the proposed method was promising for improving the pulmonary nodules computer aided diagnosis systems performance of benign and malignant pulmonary nodules.

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