Objective

2

In this paper

the spherical surface texture and nodule shape are vectorized through spherical harmonics and repulsive mapping algorithm for the benign and malignant determination of pulmonary nodule in chest CT (computed tomography) images. The current methods of deep learning during benign and malignant screening of pulmonary nodules neglect data-preprocessing while focusing more on framework improvement. So far

the depth-learning method is mainly oriented to feature information

which can be vectored. In image-processing

two kinds of targets are mainly included in two-and three-dimensional processing. In two-dimensional processing

the existence of input data must be an equal-length problem. Considering that the obtained size of the pulmonary nodules is different

we must compress large-scale images in the input process and stretch the small-scale images

which will undoubtedly affect the quality of feature extraction and the final classification results. In the classification of CT nodules for the three-dimensional treatment of pulmonary nodules

the CT image angle is more stringent due to the different sizes of pulmonary nodules and the uncertainty of growth position

and the angle factors are uncontrollable during actual CT shooting. Hence

if we insist on the characteristics of the convolution channel

it is necessary to give priority to the solution. So we have to standardize different pulmonary nodules. Different from traditional pulmonary nodule classification method

the proposed method aims on how to vectorize the spherical texture and nodule shape to allow input of data to the depth forest for feature classification training.

Method

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First

the three-dimensional pulmonary nodule images are produced by three-dimensional reconstruction of the data from the Affiliated Hospital of Liaoning University of Traditional Chinese Medicine. The data is divided into training and test set at 8:2 ratio. Second

the spherical harmonic function and the repulsive mapping algorithm are used to map the texture to the standard sphere in a mesh manner while preserving the spatial information. Third

the texture features of the pulmonary nodules are calculated by the method of mesh-LBP (local binary pattern) and vectorized by the construction ring. Then

the shape energy loss is constructed by the distance difference between the center of gravity and the central point of the pulmonary nodule during reconstruction of the three-dimensional pulmonary nodules

and the shape energy loss of the lung nodules is extracted and vectorized during rebuilding. Finally

a mesh-based multi-grain scanning method is proposed to improve the deep forest training framework by constructing multi-scale concentric ordered rings instead of traditional multi-grain scanning methods. The vectorized texture and shape features are added to the improved deep forest training framework for experimental verification.

Result

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The pulmonary nodule data of this trial was mainly provided by the IRB (institutional review committee) of the Affiliated Hospital of Liaoning University of Traditional Chinese Medicine. The approval notice number is 2017111CS(KT)-040-01. According to the growth of pulmonary nodules

the dataset was divided into four types:independent standing lung nodules

and ground glass-shaped

vascular adhesion

and thoracic adhesion type pulmonary nodules. The benign and malignant lung nodules divided into five levels:1-benign

2-suspected benign

3-unknown

4-suspected malignant

and 5-malignant. A total of 7 326 three-dimensional lung nodules were extracted as the data set for this experiment. According to several experimental results

this algorithm have a better performance than the existing advanced methods (Variational Auto-Encoder

Faster Region Convolutional neural network

GcForest deep forest

Deep Convolutional Generative Adversarial Networks

and Extreme learning machine) under the following indices:accuracy (ACC)

specificity (SPE)

sensitivity (SEN)

and AUC (area under the receiver operating characteristic ROC curve) values. In the experiments

ACC

SPE

SEN

and AUC reached 76.06%

69.46%

88.46%

and 0.84

respectively. Accuracy indicates the proportion of the classifier's correct classification of positive and negative examples

which can measure the judgment of the classifier on the sample. Specificity refers to the true negative rate

which indicates the proportion of the classifier to the correct classification of the counterexample and can measure the ability of the classifier to identify the counterexample. Sensitivity refers to the true positive rate

which indicates the proportion of correct classification of positive examples and can measure the ability of the classifier to identify positive examples. It also indicates the area under the ROC curve. The ROC curve can dynamically display the classification result. The true positive rate (sensitivity) is the ordinate

whereas the false positive rate (1-specificity) is the abscissa. A value closer to the upper left corner indicates a stronger classification ability of the method. To accurately measure the ROC curve

it can be represented by the AUC value

which is generally between 0.5 and 1. A larger value indicates a stronger classification ability. Comparatively

the method used in this paper is sub optimal for the SEN value but the method used in this article maximizes SPE and ACC values.The AUC value is also sub optimal.

Conclusion

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Lung cancer have one of the fastest growing morbidity and mortality rates worldwide and is a serious threat to people's health and life. The incidence of malignant pulmonary nodules is concealed. Many patients are often in the local late stage or even distant metastasis at the time of diagnosis

thus losing the chance for cure. Therefore

the early detection of malignant pulmonary nodules is crucial for the chance of successful cure. Based on the spherical harmonic function and the tolerance rejection mapping algorithm

the two surface and shape of pulmonary nodules can be successfully vectored and trained. Aside from considering data pretreatment

the accuracy of benign and malignant detection of pulmonary nodules is improved by these two features. Our proposed method can solve the problem of angle normalization by using a spherical space mesh to obtain the nodular information through a spherical space mesh. In addition

this method can also obtain different texture features and shape edge feature vectors as classification features by using the loop method. It is also an effective method for feature extraction and is vectorized in three-dimensional models.