Hessian based approaches for 3D lung nodule segmentation

Gonçalves, Luis F.F.F.; Novo, Jorge; Campilho, Aurélio

doi:10.1016/j.eswa.2016.05.024

Cited by 63 publications

(35 citation statements)

References 41 publications

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“…It should be noted that the neural network trained by steepest descent with mini-batches (SDMB) in [9][10][11][12] and using (19), (22), (23) had l = 6 neurons in its hidden layer, mini-batches with a size of y = 32, a tuning factor of α = 0.0004, and a number of epochs of e = 40.…”

Section: Results Of the Comparisonmentioning

confidence: 99%

“…In [17][18][19][20], the Hessian was used for tuning. In [21][22][23][24], the Hessian was employed for segmentation. In [25][26][27][28], the Hessian was utilized for optimization.…”

Section: Introductionmentioning

confidence: 99%

“…We tune the neural network with a tuning factor of α and l neurons in the hidden layer, and we use e epochs. In this kind of tuning, we divide the training data into mini-batches as in (22,23).…”

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confidence: 99%

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Hessian with Mini-Batches for Electrical Demand Prediction

et al. 2020

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The steepest descent method is frequently used for neural network tuning. Mini-batches are commonly used to get better tuning of the steepest descent in the neural network. Nevertheless, steepest descent with mini-batches could be delayed in reaching a minimum. The Hessian could be quicker than the steepest descent in reaching a minimum, and it is easier to achieve this goal by using the Hessian with mini-batches. In this article, the Hessian is combined with mini-batches for neural network tuning. The discussed algorithm is applied for electrical demand prediction.

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Section: Results Of the Comparisonmentioning

confidence: 99%

“…In [17][18][19][20], the Hessian was used for tuning. In [21][22][23][24], the Hessian was employed for segmentation. In [25][26][27][28], the Hessian was utilized for optimization.…”

Section: Introductionmentioning

confidence: 99%

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Hessian with Mini-Batches for Electrical Demand Prediction

et al. 2020

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“…In the morphology method, in order to remove the nodule-attached vessels, morphological operations were applied and lung nodules were then isolated according to the selection of connected regions [18,19]. Further, in order to better separate the lung wall from the juxtapleural nodules, a morphological operation combining the shape hypothesis was introduced, replacing the fixed size morphological template [20,21].…”

Section: Related Workmentioning

confidence: 99%

Dual-branch residual network for lung nodule segmentation

Cao

Liu

Hung

et al. 2020

Applied Soft Computing

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An accurate segmentation of lung nodules in computed tomography (CT) images is critical to lung cancer analysis and diagnosis. However, due to the variety of lung nodules and the similarity of visual characteristics between nodules and their surroundings, a robust segmentation of nodules becomes a challenging problem. In this study, we propose the Dual-branch Residual Network (DB-ResNet) which is a data-driven model. Our approach integrates two new schemes to improve the generalization capability of the model: 1) the proposed model can simultaneously capture multi-view and multi-scale features of different nodules in CT images; 2) we combine the features of the intensity and the convolution neural networks (CNN). We propose a pooling method, called the central intensity-pooling layer (CIP), to extract the intensity features of the center voxel of the block, and then use the CNN to obtain the convolutional features of the center voxel of the block. In addition, we designed a weighted sampling strategy based on the boundary of nodules for the selection of those voxels using the weighting score, to increase the accuracy of the model. The proposed method has been extensively evaluated on the LIDC dataset containing 986 nodules. Experimental results show that the DB-ResNet achieves superior segmentation performance with an average dice score of 82.74% on the dataset. Moreover, we compared our results with those of four radiologists on the same dataset.The comparison showed that our average dice score was 0.49% higher than that of human experts. This proves that our proposed method is as good as the experienced radiologist.

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“…The least value got SVM _CNN and the value is 65.78% . Goncalves et al, (2016) approaches 3D lung nodule segmentation with Hessian based matrix. He proposed a method validated with 569 solid nodules, provided accurate pulmonary nodule volumes for posterior characters.…”

Section: F Scorementioning

confidence: 99%

A Novel Hybrid Feature Extraction Model for Classification on Pulmonary Nodules

Kailasam

Sathik²

2019

Asian Pac J Cancer Prev

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In this paper an improved Computer Aided Design system can offer a second opinion to radiologists on early diagnosis of pulmonary nodules on CT (Computer Tomography) images. A Deep Convolutional Neural Network (DCNN) method is used for feature extraction and hybridize as combination of Convolutional Neural Network (CNN), Histogram of Oriented Gradient (HOG), Extended Histogram of Oriented Gradients (ExHOG) and Local Binary Pattern (LBP). A combination of shape, texture, scaling, rotation, translation features extracted using HOG, LBP and CNN. The Homogeneous descriptors used to extract the feature of lung images from Lung Image Database Consortium (LIDC) are given to classifiers Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Tree and Random Forest to classify nodules and non-nodules. Experimental results demonstrate the effectiveness of the proposed method in terms of accuracy which gives best result than the competing methods.

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Hessian based approaches for 3D lung nodule segmentation

Cited by 63 publications

References 41 publications

Hessian with Mini-Batches for Electrical Demand Prediction

Hessian with Mini-Batches for Electrical Demand Prediction

Dual-branch residual network for lung nodule segmentation

A Novel Hybrid Feature Extraction Model for Classification on Pulmonary Nodules

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