A convolutional neural network algorithm for breast tumor detection with magnetic detection electrical impedance tomography

2021 ◽  
Vol 92 (6) ◽  
pp. 064701
Author(s):  
Ruijuan Chen ◽  
Songsong Zhao ◽  
Weiwei Wu ◽  
Zhihui Sun ◽  
Jinhai Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Electrical Impedance Tomography ◽  
Breast Tumor ◽  
Electrical Impedance ◽  
Tumor Detection ◽  
Impedance Tomography ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Magnetic Detection

scholarly journals Electrical Impedance Tomography Image Reconstruction using Convolutional Neural Network with Periodic Padding

2021 ◽  
Vol 54 (15) ◽  
pp. 418-423
Author(s):  
Guilherme C. Duran ◽  
André K. Sato ◽  
Edson K. Ueda ◽  
Rogério Y. Takimoto ◽  
Thiago C. Martins ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Convolutional Neural Network ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Impedance Tomography ◽  
Tomography Image ◽  
Electrical Impedance Tomography Image

scholarly journals Deep Learning-Based CT Images in Pulmonary Function Assessment of Patients Who Underwent Laparoscopic Surgery under Guidance of Electrical Impedance Tomography

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chen Zhu ◽  
Bin Wang ◽  
Lei Li ◽  
Tianzuo Li
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Laparoscopic Surgery ◽  
Pulmonary Function ◽  
Convolutional Neural Network ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Control Group ◽  
Impedance Tomography ◽  
Function Assessment

This research aimed to study the application of CT images based on deep learning in pulmonary function assessment of patients who underwent laparoscopic surgery under the guidance of electrical impedance tomography (EIT). Sixty patients undergoing laparoscopic surgery were taken as the research subjects, who were randomly labelled as control group and experimental group. Based on deep learning, the empty convolution-combined fully convolutional neural network optimization algorithm (ECFCNN) was proposed, which was adopted to evaluate the pulmonary function of 60 patients and was compared with convolutional neural network (CNN) algorithm. The clarity of the edge contour of the image segmented by ECFCNN was better than that segmented by CNN. Average arterial pressure (MAP) and heart rate (HR) were recorded before induction (T1), 10 min before pneumoperitoneum (T2), 10 min after pneumoperitoneum (T3), 10 min before extubation (T4), and 10 min after extubation (T5), respectively. Oxygenation index (PaO2/FiO2), alveolary-arterial partial pressure of oxygen (A-ADO2), and respiratory index (RI) were recorded. The sharpness of the segmentation image edge contour of the algorithm model in this study was higher than that of the convolutional neural network. Compared with T1, T2-T4 MAP in 2 groups was decreased ( P < 0.05 ). Compared with T1, T2-T5 HR was significantly decreased ( P < 0.05 ). Compared with T2, T5 PaO2/FiO2 in control group was significantly decreased ( P < 0.05 ). Compared with the control group, T5 A-aDO2 was decreased ( P < 0.05 ). To sum up, EIT-guided lung protective ventilation can assess the pulmonary function of patients who underwent laparoscopic surgery, reduce the incidence of atelectasis, and improve postoperative lung oxygenation.

scholarly journals EIT-4LDNN: A Novel Neural Network For Electrical Impedance Tomography

2021 ◽  
Vol 1757 (1) ◽  
pp. 012013
Author(s):  
Xinyu Zhang ◽  
Xiaoyan Chen ◽  
Zichen Wang ◽  
Miao Zhang
Keyword(s):  
Neural Network ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Impedance Tomography
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