Identification of Benign and Malignant Lung Nodules in CT Images Based on Ensemble Learning Method

2021 ◽  
Author(s):  
Yifei Xu ◽  
Shijie Wang ◽  
Xiaoqian Sun ◽  
Yanjun Yang ◽  
Jiaxing Fan ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Ct Images ◽  
Lung Nodules ◽  
Learning Method

scholarly journals Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images

2009 ◽  
Vol 56 (7) ◽  
pp. 1810-1820 ◽  
Author(s):  
Xujiong Ye ◽  
Xinyu Lin ◽  
J. Dehmeshki ◽  
G. Slabaugh ◽  
G. Beddoe
Keyword(s):  
Ct Images ◽  
Lung Nodules ◽  
Computer Aided Detection ◽  
Thoracic Ct ◽  
Computer Aided

scholarly journals Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4595
Author(s):  
Parisa Asadi ◽  
Lauren E. Beckingham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Ct Images ◽  
Ct Imaging ◽  
Learning Method ◽  
Learning Methods ◽  
X Ray ◽  
Machine Learning Methods ◽  
Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

Interpretable Multi-modal Stacking-based Ensemble Learning Method for Real Estate Appraisal

2021 ◽  
pp. 1-1
Author(s):  
Sutong Wang ◽  
Jiacheng Zhu ◽  
Yunqiang Yin ◽  
Dujuan Wang ◽  
T.C. Edwin Cheng ◽  
...  
Keyword(s):  
Real Estate ◽  
Ensemble Learning ◽  
Learning Method ◽  
Real Estate Appraisal
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