Performance analysis of a computer-aided detection system for lung nodules in CT at different slice thicknesses

Narayanan, Barath Narayanan; Hardie, Russell C.; Kebede, Temesguen M.

doi:10.1117/1.jmi.5.1.014504

Cited by 37 publications

(20 citation statements)

References 37 publications

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“…Although image size is a simple factor, its effect on the accuracy of CADx was large in our study. Similar results were obtained in the previous study, where slice thickness of CT images could affect the detectability of CADe [ 34 ]. We speculated that, because VGG-16 was originally pretrained with an image size of 224 × 224, the best accuracy was obtained by finetuning VGG-16 with 2D CT images of the same size in our study.…”

Section: Discussionsupporting

confidence: 90%

Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning

et al. 2018

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We developed a computer-aided diagnosis (CADx) method for classification between benign nodule, primary lung cancer, and metastatic lung cancer and evaluated the following: (i) the usefulness of the deep convolutional neural network (DCNN) for CADx of the ternary classification, compared with a conventional method (hand-crafted imaging feature plus machine learning), (ii) the effectiveness of transfer learning, and (iii) the effect of image size as the DCNN input. Among 1240 patients of previously-built database, computed tomography images and clinical information of 1236 patients were included. For the conventional method, CADx was performed by using rotation-invariant uniform-pattern local binary pattern on three orthogonal planes with a support vector machine. For the DCNN method, CADx was evaluated using the VGG-16 convolutional neural network with and without transfer learning, and hyperparameter optimization of the DCNN method was performed by random search. The best averaged validation accuracies of CADx were 55.9%, 68.0%, and 62.4% for the conventional method, the DCNN method with transfer learning, and the DCNN method without transfer learning, respectively. For image size of 56, 112, and 224, the best averaged validation accuracy for the DCNN with transfer learning were 60.7%, 64.7%, and 68.0%, respectively. DCNN was better than the conventional method for CADx, and the accuracy of DCNN improved when using transfer learning. Also, we found that larger image sizes as inputs to DCNN improved the accuracy of lung nodule classification.

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Section: Discussionsupporting

confidence: 90%

Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning

et al. 2018

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“…With the advent of deep learning methods in pattern recognition applications, some scholars have applied them to cluster analysis. For example, in Reference [9], by studying the performance of a CAD system for lung nodules in Computed tomography (CT) as a function of slice thickness, a method of comparing the performance of CAD systems using a training method using nonuniform data was proposed.…”

Section: Introductionmentioning

confidence: 99%

Research on K-Value Selection Method of K-Means Clustering Algorithm

2019

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Among many clustering algorithms, the K-means clustering algorithm is widely used because of its simple algorithm and fast convergence. However, the K-value of clustering needs to be given in advance and the choice of K-value directly affect the convergence result. To solve this problem, we mainly analyze four K-value selection algorithms, namely Elbow Method, Gap Statistic, Silhouette Coefficient, and Canopy; give the pseudo code of the algorithm; and use the standard data set Iris for experimental verification. Finally, the verification results are evaluated, the advantages and disadvantages of the above four algorithms in a K-value selection are given, and the clustering range of the data set is pointed out.

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“…As stated previously, a CAD system consists of several modules, which are (1) image enhancement, (2) image segmentation, (3) feature extraction, (4) feature selection (FS), (5) classification, and (6) an evaluation of the classifiers [18,19]. This proposed CAD system enhances images using a contrast enhancement method named contrast-limited adaptive histogram equalization (CLAHE) [20].…”

Section: Methodsmentioning

confidence: 99%

Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers

2019

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Breast cancer is one of the major health issues across the world. In this study, a new computer-aided detection (CAD) system is introduced. First, the mammogram images were enhanced to increase the contrast. Second, the pectoral muscle was eliminated and the breast was suppressed from the mammogram. Afterward, some statistical features were extracted. Next, k-nearest neighbor (k-NN) and decision trees classifiers were used to classify the normal and abnormal lesions. Moreover, multiple classifier systems (MCS) was constructed as it usually improves the classification results. The MCS has two structures, cascaded and parallel structures. Finally, two wrapper feature selection (FS) approaches were applied to identify those features, which influence classification accuracy. The two data sets (1) the mammographic image analysis society digital mammogram database (MIAS) and (2) the digital mammography dream challenge were combined together to test the CAD system proposed. The highest accuracy achieved with the proposed CAD system before FS was 99.7% using the Adaboosting of the J48 decision tree classifiers. The highest accuracy after FS was 100 %, which was achieved with k‐NN classifier. Moreover, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed CAD system was able to accurately classify normal and abnormal lesions in mammogram samples.

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Performance analysis of a computer-aided detection system for lung nodules in CT at different slice thicknesses

Cited by 37 publications

References 37 publications

Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning

Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning

Research on K-Value Selection Method of K-Means Clustering Algorithm

Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers

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