Detection of Solitary Pulmonary Nodules Based on Brain-Computer Interface

Qiu, Shi; Li, Junjun; Cong, Mengdi; Wu, Chun; Qin, Yan; Liang, Ting

doi:10.1155/2020/4930972

Cited by 8 publications

(7 citation statements)

References 34 publications

(35 reference statements)

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“…It can be seen that the MIP algorithm can better reflect the boundary features of pulmonary nodules and improve the distinguishing effect of spiculation sign. The fractal model (FM) [ 34 ] uses the fractal operator to calculate the fractal degree of pulmonary nodules to judge the signs of pulmonary nodules. The nerve network model (NNM) [ 10 ] algorithm introduces a learning mechanism to realize feature learning, which requires a large number of samples to train parameters.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

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Spiculation Sign Recognition in a Pulmonary Nodule Based on Spiking Neural P Systems

Qiu

Sun

Zhou

et al. 2020

BioMed Research International

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The spiculation sign is one of the main signs to distinguish benign and malignant pulmonary nodules. In order to effectively extract the image feature of a pulmonary nodule for the spiculation sign distinguishment, a new spiculation sign recognition model is proposed based on the doctors’ diagnosis process of pulmonary nodules. A maximum density projection model is established to fuse the local three-dimensional information into the two-dimensional image. The complete boundary of a pulmonary nodule is extracted by the improved Snake model, which can take full advantage of the parallel calculation of the Spike Neural P Systems to build a new neural network structure. In this paper, our experiments show that the proposed algorithm can accurately extract the boundary of a pulmonary nodule and effectively improve the recognition rate of the spiculation sign.

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Section: Experiments and Results Analysismentioning

confidence: 99%

“…Shakir et al [ 33 ] establish a three-dimensional level set algorithm based on the two-dimensional segmentation. Qiu et al [ 34 ] classify pulmonary nodules based on the geometric theory. Xie et al [ 35 ] fuse multiple features to distinguish pulmonary nodules.…”

Section: Introductionmentioning

confidence: 99%

Spiculation Sign Recognition in a Pulmonary Nodule Based on Spiking Neural P Systems

Qiu

Sun

Zhou

et al. 2020

BioMed Research International

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“…MRI is featured by multilayer, multisequence, and intensified signal characterizers. Its diagnosis rate of a pulmonary nodule with d > 5 mm attains 100% [ 17 ]. The study results herein manifested that, in terms of the proportion of patients in time-signal curve types of different MRI images, there was no statistical difference between the benign and malignant SPN patients ( P > 0.05).…”

Section: Discussionmentioning

confidence: 99%

MRI Image Segmentation Model with Support Vector Machine Algorithm in Diagnosis of Solitary Pulmonary Nodule

Feng¹,

Zhang²,

Zhu³

et al. 2021

Contrast Media & Molecular Imaging

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This study focused on the application value of MRI images processed by a Support Vector Machine (SVM) algorithm-based model in diagnosis of benign and malignant solitary pulmonary nodule (SPN). The SVM algorithm was constrained by a self-paced regularization item and gradient value to establish the MRI image segmentation model (SVM-L) for lung. Its performance was compared factoring into the Dice index (DI), sensitivity (SE), specificity (SP), and Mean Square Error (MSE). 28 SPN patients who underwent the parallel MRI examination were selected as research subjects and were divided into the benign group (11 patients) and malignant group (17 patients) according to different plans for diagnosis and treatment. The apparent diffusion coefficient (ADC) at different b values was analyzed, and the steepest slope (SS) and washout ratio (WR) values in the two groups were calculated. The result showed that the MSE, DI, SE, SP values, and operation time of the SVM-L model were (0.41 ± 0.02), (0.84 ± 0.13), (0.89 ± 0.04), (0.993 ± 0.004), and (30.69 ± 2.60)s, respectively, apparently superior to those of the other algorithms, but there were no statistic differences ( P > 0.05 ) in the WR value between the two groups of patients. The SS values of the time-signal curve in the benign and malignant groups were (2.52 ± 0.69) %/s and (3.34 ± 00.41) %/s, respectively. Obviously, the SS value of the benign group was significantly lower than that of the malignant group ( P < 0.01 ). The ADC value with different b values in the benign group was significantly lower than that of the malignant group ( P < 0.01 ). It suggested that the SVM-L model significantly improved the quality of lung MRI images and increased the accuracy to differentiate benign and malignant SPN, providing reference for the diagnosis and treatment of SPN patients.

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“…Rossini et al ( 2020 ) proposed markers for early Alzheimer's disease diagnosis, demonstrating the validity of the EEG analysis. Qiu et al ( 2020 ) analyzed the EEG transmission process. Oltu et al ( 2021 ) proposed a novel Alzheimer's disease detection algorithm based on EEG.…”

Section: Introductionmentioning

confidence: 99%

Alzheimer's Disease Analysis Algorithm Based on No-threshold Recurrence Plot Convolution Network

Zhou

Qiu

2022

Front. Aging Neurosci.

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Alzheimer's disease is a neurological disorder characterized by progressive cognitive dysfunction and behavioral impairment that occurs in old. Early diagnosis and treatment of Alzheimer's disease is great significance. Electroencephalography (EEG) signals can be used to detect Alzheimer's disease due to its non-invasive advantage. To solve the problem of insufficient analysis by single-channel EEG signal, we analyze the relationship between multiple channels and build PLV framework. To solve the problem of insufficient representation of 1D signal, a threshold-free recursive plot convolution network was constructed to realize 2D representation. To solve the problem of insufficient EEG signal characterization, a fusion algorithm of clinical features and imaging features was proposed to detect Alzheimer's disease. Experimental results show that the algorithm has good performance and robustness.

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Detection of Solitary Pulmonary Nodules Based on Brain-Computer Interface

Cited by 8 publications

References 34 publications

Spiculation Sign Recognition in a Pulmonary Nodule Based on Spiking Neural P Systems

Spiculation Sign Recognition in a Pulmonary Nodule Based on Spiking Neural P Systems

MRI Image Segmentation Model with Support Vector Machine Algorithm in Diagnosis of Solitary Pulmonary Nodule

Alzheimer's Disease Analysis Algorithm Based on No-threshold Recurrence Plot Convolution Network

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