A Hybrid Model: DGnet-SVM for the Classification of Pulmonary Nodules

2017 ◽  
pp. 732-741 ◽  
Author(s):  
Yixuan Xu ◽  
Guokai Zhang ◽  
Yuan Li ◽  
Ye Luo ◽  
Jianwei Lu
Keyword(s):  
Hybrid Model ◽  
Pulmonary Nodules

Bioimpedance Spectroscopy Measurement and Classification of Lung Tissue to Identify Pulmonary Nodules

2021 ◽  
pp. 1-1
Author(s):  
Rasool Baghbani ◽  
Mohammad Behgam Shadmehr ◽  
Masoomeh Ashoorirad ◽  
Seyyedeh Fatemeh Molaeezadeh ◽  
Mohammad Hassan Moradi
Keyword(s):  
Lung Tissue ◽  
Pulmonary Nodules ◽  
Bioimpedance Spectroscopy ◽  
Spectroscopy Measurement

Treatment Implications of a Hybrid Model for the Classification of Alcoholism

1992 ◽  
pp. 257-282
Author(s):  
Leslie C. Morey ◽  
Janice K. Jones
Keyword(s):  
Hybrid Model

Automatic benign and malignant classification of pulmonary nodules in thoracic computed tomography based on RF algorithm

2018 ◽  
Vol 12 (7) ◽  
pp. 1253-1264 ◽  
Author(s):  
Xiang-Xia Li ◽  
Bin Li ◽  
Lian-Fang Tian ◽  
Li Zhang
Keyword(s):  
Computed Tomography ◽  
Pulmonary Nodules ◽  
Thoracic Computed Tomography

scholarly journals Hybrid Scheme for Automated Classification of Pulmonary Nodules Using PET/CT Images and Patient Information

2020 ◽  
Vol 10 (12) ◽  
pp. 4225
Author(s):  
Ayumi Yamada ◽  
Atsushi Teramoto ◽  
Masato Hoshi ◽  
Hiroshi Toyama ◽  
Kazuyoshi Imaizumi ◽  
...  
Keyword(s):  
Patient Information ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Machine Learning Algorithms ◽  
Automated Classification ◽  
Hybrid Scheme ◽  
Laboratory Test Results ◽  
Pet Ct ◽  
Functional Features

The classification of pulmonary nodules using computed tomography (CT) and positron emission tomography (PET)/CT is often a hard task for physicians. To this end, in our previous study, we developed an automated classification method using PET/CT images. In actual clinical practice, in addition to images, patient information (e.g., laboratory test results) is available and may be useful for automated classification. Here, we developed a hybrid scheme for automated classification of pulmonary nodules using these images and patient information. We collected 36 conventional CT images and PET/CT images of patients who underwent lung biopsy following bronchoscopy. Patient information was also collected. For classification, 25 shape and functional features were first extracted from the images. Benign and malignant nodules were identified using machine learning algorithms along with the images’ features and 17 patient-information-related features. In the leave-one-out cross-validation of our hybrid scheme, 94.4% of malignant nodules were identified correctly, and 77.7% of benign nodules were diagnosed correctly. The hybrid scheme performed better than that of our previous method that used only image features. These results indicate that the proposed hybrid scheme may improve the accuracy of malignancy analysis.

Evaluation of data balancing techniques in 3D CNNs for the classification of pulmonary nodules in CT images

2020 ◽  
Author(s):  
Thiago Jose Barbosa Lima ◽  
Flavio Henrique Duarte de Araiujo ◽  
Antonio Oseas de Carvalho Filho ◽  
Ricardo de Andrade Lira Rabelo ◽  
Rodrigo de Melo Souza Veras ◽  
...  
Keyword(s):  
Pulmonary Nodules ◽  
Ct Images ◽  
Evaluation Of Data
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