scholarly journals Automated detection and localization system of myocardial infarction in single-beat ECG using Dual-Q TQWT and wavelet packet tensor decomposition

2020 ◽  
Vol 184 ◽  
pp. 105120 ◽  
Author(s):  
Jia Liu ◽  
Chi Zhang ◽  
Yongjie Zhu ◽  
Tapani Ristaniemi ◽  
Tiina Parviainen ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Wavelet Packet ◽  
Tensor Decomposition ◽  
Automated Detection ◽  
Localization System ◽  
Detection And Localization

Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads

2016 ◽  
Vol 99 ◽  
pp. 146-156 ◽  
Author(s):  
U. Rajendra Acharya ◽  
Hamido Fujita ◽  
Vidya K. Sudarshan ◽  
Shu Lih Oh ◽  
Muhammad Adam ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Comparative Study ◽  
Automated Detection ◽  
Detection And Localization

scholarly journals Automated Detection and Localization of Myocardial Infarction With Staked Sparse Autoencoder and TreeBagger

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 70634-70642 ◽  
Author(s):  
Jieshuo Zhang ◽  
Feng Lin ◽  
Peng Xiong ◽  
Haiman Du ◽  
Hong Zhang ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Automated Detection ◽  
Sparse Autoencoder ◽  
Detection And Localization

scholarly journals Orchard Mapping with Deep Learning Semantic Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3813
Author(s):  
Athanasios Anagnostis ◽  
Aristotelis C. Tagarakis ◽  
Dimitrios Kateris ◽  
Vasileios Moysiadis ◽  
Claus Grøn Sørensen ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Automated Detection ◽  
Aerial Images ◽  
Training Dataset ◽  
Field Boundary ◽  
Different Seasons ◽  
Detection And Localization ◽  
Different Levels

This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection and localization of the canopy of orchard trees under various conditions (i.e., different seasons, different tree ages, different levels of weed coverage). The implemented dataset was composed of images from three different walnut orchards. The achieved variability of the dataset resulted in obtaining images that fell under seven different use cases. The best-trained model achieved 91%, 90%, and 87% accuracy for training, validation, and testing, respectively. The trained model was also tested on never-before-seen orthomosaic images or orchards based on two methods (oversampling and undersampling) in order to tackle issues with out-of-the-field boundary transparent pixels from the image. Even though the training dataset did not contain orthomosaic images, it achieved performance levels that reached up to 99%, demonstrating the robustness of the proposed approach.

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