Pulmonary Nodule Recognition Research Articles

Interpretable Optimization Training Strategy-Based DCNN and Its Application on CT Image Recognition

The theoretical basis of the discrete random sample batch classification is not clear and the sample division is not scientific during the process of Deep Convolutional Neural Network (DCNN) model training. Aiming at the problems above, starting from the DCNN detection recognition mechanism, the theory of random discrete samples is given and proved, and a scientific quantitative batch of sample input method is proposed. Combined with image preprocessing, based on the strategy of random dispersion of samples, and scientifically quantified sample input batches, the DCNN model is trained with limited label samples, and then the CT image recognition of pulmonary nodules is carried out. Experimental results based on the LIDC-ID-RI public dataset show that the sensitivity, specificity, and accuracy of the proposed method have reached 96.40%, 95.60%, and 96.00%, respectively. Compared with the multiscale convolutional neural network method and the multiscale multimode image fusion method, the recognition accuracy of the proposed method is improved by 1.6 and 3.49 percentage points, respectively.

Improved cost-sensitive multikernel learning support vector machine algorithm based on particle swarm optimization in pulmonary nodule recognition

In the lung computer-aided detection (Lung CAD) system, the region of interest (ROI) of lung nodules has more false positives, making the imbalance between positive and negative (true positive and false positive) samples more likely to lead to misclassification of true positive nodules, a cost-sensitive multikernel learning support vector machine (CS-MKL-SVM) algorithm is proposed. Different penalty coefficients are assigned to positive and negative samples, so that the model can better learn the features of true positive nodules and improve the classification effect. To further improve the detection rate of pulmonary nodules and overall recognition accuracy, a score function named F-new based on the harmonic mean of accuracy (ACC) and sensitivity (SEN) is proposed as a fitness function for subsequent particle swarm optimization (PSO) parameter optimization, and a feasibility analysis of this function is performed. Compared with the fitness function that considers only accuracy or sensitivity, both the detection rate and the recognition accuracy of pulmonary nodules can be improved by this new algorithm. Compared with the grid search algorithm, using PSO for parameter search can reduce the model training time by nearly 20 times and achieve rapid parameter optimization. The maximum F-new obtained on the test set is 0.9357 for the proposed algorithm. When the maximum value of F-new is achieved, the corresponding recognition ACC is 91%, and SEN is 96.3%. Compared with the radial basis function in the single kernel, the F-new of the algorithm in this paper is 2.16% higher, ACC is 1.00% higher and SEN is equal. Compared with the polynomial kernel function in the single kernel, the F-new of the algorithm is 3.64% higher, ACC is 1.00% higher and SEN is 7.41% higher. The experimental results show that the F-new, ACC and SEN of the proposed algorithm is the best among them, and the results obtained by using multikernel function combined with F-new index are better than the single kernel function. Compared with the MKL-SVM algorithm of grid search, the ACC of the algorithm in this paper is reduced by 1%, and the results are equal to those of the MKL-SVM algorithm based on PSO only. Compared with the above two algorithms, SEN is increased by 3.71% and 7.41%, respectively. Therefore, it can be seen that the cost sensitive method can effectively reduce the missed detection of nodules, and the availability of the new algorithm can be further verified.

Research on Lung Nodule Recognition Based on Stacking Algorithm

In recent years, the incidence of cancer has been increasing year by year, and lung cancer has ranked the first place. In order to detect lung nodules quickly and accurately, since the lung CT images in the data set used in this experiment are original medical images, image preprocessing is first required, including noise reduction, segmentation and region of interest extraction, Therefore, this paper proposes a lung nodule recognition method based on Stacking algorithm to detect lung nodules quickly and accurately. Secondly, the three-dimensional gray co-occurrence matrix texture features of the region of interest are extracted. Finally, the Stacking evaluation model is used to identify and classify lung nodules.The experimental results show: Under ten-fold cross-validation, the training accuracy rate and the test accuracy rate can reach 90.9%,82.3%, respectively.This method is feasible for pulmonary nodule recognition and provides data reference value for doctors' auxiliary diagnosis.

Texture recognition of pulmonary nodules based on volume local direction ternary pattern

ABSTRACT In recent years, the incidence of lung cancer has been increasing. Lung cancer detection is based on computed tomography (CT) imaging of the lung area to determine whether there are pulmonary nodules. And then judge what’s good and what’s bad. However, due to the traditional way of manual reading and lack of experience and other problems. This leads to visual fatigue and misdiagnosis and missed diagnosis. In order to detect pulmonary nodules early and accurately, a new assistant diagnosis method for pulmonary nodules is proposed. Firstly, the image is preprocessed and denoised by median filter, the lung parenchyma is segmented by random walk algorithm and the region of interest is extracted, and then, according to the continuity of the CT slices, the texture feature extraction method of pulmonary nodules based on volume local direction ternary pattern is used to extract the features. Finally, the pulmonary nodules are identified and classified by the assistant diagnosis model of pulmonary nodules based on Stacking algorithm. In order to illustrate the validity of the diagnosis model, the experiments are carried out by cross-validation of ten folds. Experiments using data from LIDC database show that the accuracy, sensitivity and specificity of the proposed method are 82.2%, 85.7%, and 78.8%, respectively. Texture Recognition method based on volume vocal direction ternary pattern is feasible for the identification of pulmonary nodules and provides a reference value for doctor-assisted diagnosis.

基于三维卷积神经网络的肺结节识别研究

基于三维卷积神经网络的肺结节识别研究

Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO.

Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified.

Correlation coefficient based supervised locally linear embedding for pulmonary nodule recognition

Background and objectiveDimensionality reduction techniques are developed to suppress the negative effects of high dimensional feature space of lung CT images on classification performance in computer aided detection (CAD) systems for pulmonary nodule detection. MethodsAn improved supervised locally linear embedding (SLLE) algorithm is proposed based on the concept of correlation coefficient. The Spearman's rank correlation coefficient is introduced to adjust the distance metric in the SLLE algorithm to ensure that more suitable neighborhood points could be identified, and thus to enhance the discriminating power of embedded data. The proposed Spearman's rank correlation coefficient based SLLE (SC2SLLE) is implemented and validated in our pilot CAD system using a clinical dataset collected from the publicly available lung image database consortium and image database resource initiative (LICD-IDRI). Particularly, a representative CAD system for solitary pulmonary nodule detection is designed and implemented. After a sequential medical image processing steps, 64 nodules and 140 non-nodules are extracted, and 34 representative features are calculated. The SC2SLLE, as well as SLLE and LLE algorithm, are applied to reduce the dimensionality. Several quantitative measurements are also used to evaluate and compare the performances. ResultsUsing a 5-fold cross-validation methodology, the proposed algorithm achieves 87.65% accuracy, 79.23% sensitivity, 91.43% specificity, and 8.57% false positive rate, on average. Experimental results indicate that the proposed algorithm outperforms the original locally linear embedding and SLLE coupled with the support vector machine (SVM) classifier. ConclusionsBased on the preliminary results from a limited number of nodules in our dataset, this study demonstrates the great potential to improve the performance of a CAD system for nodule detection using the proposed SC2SLLE.

Analysis of Factors Reducing the Accuracy of Pulmonary Nodule Recognition in Thoracic X-ray CT Images Using the Subspace Method

Analysis of Factors Reducing the Accuracy of Pulmonary Nodule Recognition in Thoracic X-ray CT Images Using the Subspace Method

Eigen Image Recognition of Pulmonary Nodules from Thoracic CT Images by Use of Subspace Method

We have proposed a recognition method for pulmonary nodules based on experimentally selected feature values (such as contrast, circularity, etc.) of pathologic candidate regions detected by our Variable N-Quoit (VNQ) filter. In this paper, we propose a new recognition method for pulmonary nodules by use of not experimentally selected feature values, but each CT value itself in a region of interest (ROI) as a feature value. The proposed method has 2 phases: learning and recognition. In the learning phase, first, the pathologic candidate regions are classified into several clusters based on a principal component score. This score is calculated from a set of CT values in the ROI that are regarded as a feature vector, and then eigen vectors and eigen values are calculated for each cluster by application of principal component analysis to the cluster. The eigen vectors (we call them eigen-images) corresponding to the S-th largest eigen values are utilized as base vectors for subspaces of the clusters in a feature space. In the recognition phase, correlations are measured between the feature vector derived from testing data and the subspace which is spanned by the eigen-images. If the correlation with the nodule subspace is large, the pathologic candidate region is determined to be a nodule, otherwise, it is determined to be a normal organ. In the experiment, first, we decide on the optimal number of subspace dimensions. Then, we demonstrated the robustness of our algorithm by using simulated nodule images.

Frequency-filtered image post-processing of digital luminescence radiographs in pulmonary nodule imaging

Aim: The aim of the study was to optimize unsharp masking image post-processing of digital luminescence radiographs (DLR) for the representation of pulmonary nodules, and to compare DLR to screen-film radiography at two dose levels. Patients and methods: A total of 284 CT-validated pulmonary nodules were evaluated. One hundred and forty-nine nodules were exposed with a 200-speed screen-film combination (SFC) and 135 nodules with a 400-speed SFC, with correspondingly exposed storage phosphor images. The kernal size in digital post-processing using ‘unsharp masking’ was varied between S 10 (2.83mm) and S 70 (19.80mm). A total of 11 928 individual assessments were obtained from six independent observers and evaluated in multifactorial variance analyses. Results: The large filter kernels of S 40 and S 70 were on a par with the 200-speed SFC ( P > 0.05). As the exposure dose was reduced, the quality of the digital image vis-à-vis the 400-speed SFC improved significantly ( P < 0.05). Smaller filter kernels (S 10; S 20) producing edge-enhancement processing were significantly inferior to the analog image technique in both dose ranges ( P < 0.05). Conclusions: At speed class 200, low-frequency emphasizing digital image post-processing with large filter kernels are significantly superior to high-frequency emphasizing filtrations for the recognition of pulmonary nodules. In the lower dose range DLR with large filter kernel unsharp masking processing showed significantly improved image quality compared to 400 speed SFC for the detection of pulmonary nodules.

A Perceptually Based Method for Enhancing Pulmonary Nodule Recognition

Feedback of chest areas receiving prolonged gaze durations significantly increases nodule detection performance. Why feedback circling enhances performance when other cueing methods produce equivocal results was assessed. Chest and noise images with nodule targets were used to determine: what type of cue is most effective; whether circling influences the way the eye samples the target; whether circling limits processing of distracting information outside its boundary. Circling improves performance more than cues with less complete boundaries and increases the accuracy and frequency with which nodules are fixated. Outside distractors were detected less often with than without the circle present. Circling isolates the abnormal region from the rest of the image, making disembedding and integration of nodule features more likely and insulates this region from distractors. The facilitative effects of circling are generalizable to other images in which low contrast targets are embedded in noisy backgrounds.