Radiomics-Enhanced Multi-task Neural Network for Non-invasive Glioma Subtyping and Segmentation

2020 ◽  
pp. 81-90 ◽  
Author(s):  
Zhiyuan Xue ◽  
Bowen Xin ◽  
Dingqian Wang ◽  
Xiuying Wang
Keyword(s):  
Neural Network ◽  
Non Invasive

Non-invasive quantitative diagnosis of liver fibrosis with an artificial neural network

2021 ◽  
Author(s):  
Jiaguang Song ◽  
Yuezhong Zhang ◽  
Jinling Cheng ◽  
Shi Wang ◽  
Zhi Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Liver Fibrosis ◽  
Quantitative Diagnosis ◽  
Non Invasive ◽  
Artificial Neural

Label-free non-invasive classification of rice seeds using optical coherence tomography assisted with deep neural network

2021 ◽  
Vol 137 ◽  
pp. 106861
Author(s):  
Deepa Joshi ◽  
Ankit Butola ◽  
Sheetal Raosaheb Kanade ◽  
Dilip K. Prasad ◽  
S.V. Amitha Mithra ◽  
...  
Keyword(s):  
Neural Network ◽  
Optical Coherence Tomography ◽  
Deep Neural Network ◽  
Optical Coherence ◽  
Label Free ◽  
Non Invasive ◽  
Rice Seeds

Reliable detection of atrial fibrillation with a medical wearable under inpatient conditions (CoMMoD-A-fib)

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
M Jacobsen ◽  
T.A Dembek ◽  
A.P Ziakos ◽  
G Kobbe ◽  
M Kollmann ◽  
...  
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Deep Neural Network ◽  
Sampling Rate ◽  
Public Institution ◽  
Funding Source ◽  
Upper Arm ◽  
Recording Time ◽  
Reduced Ejection Fraction ◽  
Non Invasive

Abstract Background Atrial fibrillation (A-fib) is the most common arrhythmia; however, detection of A-fib is a challenge due to irregular occurrence. Purpose Evaluating feasibility and performance of a non-invasive medical wearable for detection of A-fib. Methods In the CoMMoD-A-fib trial admitted patients with a high risk for A-fib carried the wearable and an ECG Holter (control) in parallel over a period of 24 hours under not physically restricted conditions. The wearable with a tight-fit upper arm band employs a photoplethysmography (PPG) technology enabling a high sampling rate. Different algorithms (including a deep neural network) were applied to 5 min PPG datasets for detection of A-fib. Proportion of monitoring time automatically interpretable by algorithms (= interpretable time) was analyzed for influencing factors. Results In 102 inpatients (age 71.0±11.9 years; 52% male) 2306 hours of parallel recording time could be obtained; 1781 hours (77.2%) of these were automatically interpretable by an algorithm analyzing PPG derived intervals. Detection of A-Fib was possible with a sensitivity of 92.7% and specificity of 92.4% (AUC 0.96). Also during physical activity, detection of A-fib was sufficiently possible (sensitivity 90.1% and specificity 91.2%). Usage of the deep neural network improved detection of A-fib further (sensitivity 95.4% and specificity 96.2%). A higher prevalence of heart failure with reduced ejection fraction was observed in patients with a low interpretable time (p=0.080). Conclusion Detection of A-fib by means of an upper arm non-invasive medical wearable with a high resolution is reliably possible under inpatient conditions. Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): Internal grant program (PhD and Dr. rer. nat. Program Biomedicine) of the Faculty of Health at Witten/Herdecke University, Germany. HELIOS Kliniken GmbH (Grant-ID 047476), Germany

Non-invasive intelligent monitoring system for fault detection in induction motor based on bio piezoelectric sensor using ANN

2022 ◽  
Author(s):  
Massine GANA ◽  
Hakim ACHOUR ◽  
Kamel BELAID ◽  
Zakia CHELLI ◽  
Mourad LAGHROUCHE ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Motor ◽  
Vibration Analysis ◽  
Low Cost ◽  
Piezoelectric Sensor ◽  
Integrated System ◽  
Easy Access ◽  
Non Invasive ◽  
Artificial Neural

Abstract This paper presents a design of a low-cost integrated system for the preventive detection of unbalance faults in an induction motor. In this regard, two non-invasive measurements have been collected then monitored in real time and transmitted via an ESP32 board. A new bio-flexible piezoelectric sensor developed previously in our laboratory, was used for vibration analysis. Moreover an infrared thermopile was used for non-contact temperature measurement. The data is transmitted via Wi-Fi to a monitoring station that intervenes to detect an anomaly. The diagnosis of the motor condition is realized using an artificial neural network algorithm implemented on the microcontroller. Besides, a Kalman filter is employed to predict the vibrations while eliminating the noise. The combination of vibration analysis, thermal signature analysis and artificial neural network provides a better diagnosis. It ensures efficiency, accuracy, easy access to data and remote control, which significantly reduces human intervention.

Non-invasive prediction of bloodstain age using the principal component and a back propagation artificial neural network

2017 ◽  
Vol 14 (9) ◽  
pp. 095601 ◽  
Author(s):  
Huimin Sun ◽  
Yaoyong Meng ◽  
Pingli Zhang ◽  
Yajing Li ◽  
Nan Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Principal Component ◽  
Non Invasive ◽  
Artificial Neural

scholarly journals Rapid identification of wood species using XRF and neural network machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Aaron N. Shugar ◽  
B. Lee Drake ◽  
Greg Kelley
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Wood Species ◽  
Cost Effective ◽  
Rapid Identification ◽  
Invasive Technique ◽  
X Ray ◽  
Non Invasive ◽  
Cost Effective Alternative

AbstractAn innovative approach for the rapid identification of wood species is presented. By combining X-ray fluorescence spectrometry with convolutional neural network machine learning, 48 different wood specimens were clearly differentiated and identified with a 99% accuracy. Wood species identification is imperative to assess illegally logged and transported lumber. Alternative options for identification can be time consuming and require some level of sampling. This non-invasive technique offers a viable, cost-effective alternative to rapidly and accurately identify timber in efforts to support environmental protection laws and regulations.

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