Bone quality classification in DXA images using pyradiomics and machine learning

2021 ◽  
Author(s):  
Mailen Gonzalez ◽  
José M. Massa ◽  
Nicolás de Martino
Keyword(s):  
Machine Learning ◽  
Bone Quality ◽  
Quality Classification

scholarly journals Interactive machine learning for soybean seed and seedling quality classification

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
André Dantas de Medeiros ◽  
Nayara Pereira Capobiango ◽  
José Maria da Silva ◽  
Laércio Junio da Silva ◽  
Clíssia Barboza da Silva ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soybean Seed ◽  
Seedling Quality ◽  
Interactive Machine Learning ◽  
Quality Classification

scholarly journals Research on Classification Method of Maize Seed Defect Based on Machine Vision

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Sheng Huang ◽  
Xiaofei Fan ◽  
Lei Sun ◽  
Yanlu Shen ◽  
Xuesong Suo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Heat Map ◽  
Deep Learning Algorithm ◽  
Quality Classification ◽  
Visualization Technology ◽  
Better Than

Traditionally, the classification of seed defects mainly relies on the characteristics of color, shape, and texture. This method requires repeated extraction of a large amount of feature information, which is not efficiently used in detection. In recent years, deep learning has performed well in the field of image recognition. We introduced convolutional neural networks (CNNs) and transfer learning into the quality classification of seeds and compared them with traditional machine learning algorithms. Experiments showed that deep learning algorithm was significantly better than the machine learning algorithm with an accuracy of 95% (GoogLeNet) vs. 79.2% (SURF+SVM). We used three classifiers in GoogLeNet to demonstrate that network accuracy increases as the depth of the network increases. We used the visualization technology to obtain the feature map of each layer of the network in CNNs and used the heat map to represent the probability distribution of the inference results. As an end-to-end network, CNNs can be easily applied for automated seed manufacturing.

Thread Quality Classification of a Tapping Machine Based on Machine Learning

2021 ◽  
pp. 1-1
Author(s):  
Chiao-Sheng Wang ◽  
I-Hsi Kao ◽  
Ya-Wen Hsu ◽  
Tsung-Chun Lin ◽  
Der-Min Tsay ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Classification
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