scholarly journals OP01.09: Automatic classification of fetal ultrasound images: a remarkable application of deep learning technology for fetal medicine

2019 ◽  
Vol 54 (S1) ◽  
pp. 86-87
Author(s):  
X.P. Burgos‐Artizuu ◽  
E. Eixarch ◽  
D. Coronado‐Gutierrez ◽  
B. Valenzuela ◽  
E. Bonet‐Carne ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Classification ◽  
Learning Technology ◽  
Ultrasound Images ◽  
Fetal Ultrasound ◽  
Fetal Medicine

scholarly journals VP25.02: Automatic classification of fetal axial brain planes from ultrasound images via deep learning

2020 ◽  
Vol 56 (S1) ◽  
pp. 162-162
Author(s):  
D. Coronado‐Gutierrez ◽  
X.P. Burgos‐Artizuu ◽  
E. Monterde ◽  
E. Eixarch ◽  
B. Valenzuela ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Classification ◽  
Ultrasound Images

A novel two-stage method of plant seedlings classification based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Tianhong Dai ◽  
Shijie Cong ◽  
Jianping Huang ◽  
Yanwen Zhang ◽  
Xinwang Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Technology ◽  
Two Stage ◽  
Second Stage ◽  
Stage Classification ◽  
Different Types ◽  
Two Stages ◽  
Plant Seedlings

In agricultural production, weed removal is an important part of crop cultivation, but inevitably, other plants compete with crops for nutrients. Only by identifying and removing weeds can the quality of the harvest be guaranteed. Therefore, the distinction between weeds and crops is particularly important. Recently, deep learning technology has also been applied to the field of botany, and achieved good results. Convolutional neural networks are widely used in deep learning because of their excellent classification effects. The purpose of this article is to find a new method of plant seedling classification. This method includes two stages: image segmentation and image classification. The first stage is to use the improved U-Net to segment the dataset, and the second stage is to use six classification networks to classify the seedlings of the segmented dataset. The dataset used for the experiment contained 12 different types of plants, namely, 3 crops and 9 weeds. The model was evaluated by the multi-class statistical analysis of accuracy, recall, precision, and F1-score. The results show that the two-stage classification method combining the improved U-Net segmentation network and the classification network was more conducive to the classification of plant seedlings, and the classification accuracy reaches 97.7%.

Automatic classification of EEG signals via deep learning

2021 ◽  
Author(s):  
Tao Wu ◽  
Xiangzeng Kong ◽  
Yiwen Wang ◽  
Xue Yang ◽  
Jingxuan Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Classification ◽  
Eeg Signals

Toward an Automatic Classification of SEM Images of Nanomaterials via a Deep Learning Approach

2019 ◽  
pp. 61-72 ◽  
Author(s):  
Cosimo Ieracitano ◽  
Fabiola Pantó ◽  
Nadia Mammone ◽  
Annunziata Paviglianiti ◽  
Patrizia Frontera ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Classification ◽  
Learning Approach ◽  
Sem Images
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