3D Oxygen‐Enhanced MRI at 3T MR System: Comparison With Thin‐Section CT of Quantitative Capability for Pulmonary Functional Loss Assessment and Clinical Stage Classification of COPD in Smokers

2020 ◽  
Author(s):  
Yoshiharu Ohno ◽  
Masao Yui ◽  
Takeshi Yoshikawa ◽  
Shinichiro Seki ◽  
Daisuke Takenaka ◽  
...  
Keyword(s):  
Thin Section ◽  
Clinical Stage ◽  
Loss Assessment ◽  
Functional Loss ◽  
Enhanced Mri ◽  
Stage Classification ◽  
3T Mr

Oxygen-enhanced MRI vs. quantitatively assessed thin-section CT: Pulmonary functional loss assessment and clinical stage classification of asthmatics

2011 ◽  
Vol 77 (1) ◽  
pp. 85-91 ◽  
Author(s):  
Yoshiharu Ohno ◽  
Hisanobu Koyama ◽  
Keiko Matsumoto ◽  
Yumiko Onishi ◽  
Munenobu Nogami ◽  
...  
Keyword(s):  
Thin Section ◽  
Clinical Stage ◽  
Loss Assessment ◽  
Functional Loss ◽  
Enhanced Mri ◽  
Stage Classification

Dynamic Oxygen-Enhanced MRI Versus Quantitative CT: Pulmonary Functional Loss Assessment and Clinical Stage Classification of Smoking-Related COPD

2008 ◽  
Vol 190 (2) ◽  
pp. W93-W99 ◽  
Author(s):  
Yoshiharu Ohno ◽  
Hisanobu Koyama ◽  
Munenobu Nogami ◽  
Daisuke Takenaka ◽  
Sumiaki Matsumoto ◽  
...  
Keyword(s):  
Clinical Stage ◽  
Loss Assessment ◽  
Quantitative Ct ◽  
Functional Loss ◽  
Enhanced Mri ◽  
Stage Classification

scholarly journals Some Problems in practice of clinical stage classification of Japan Lung Cancer Society

Haigan ◽  
1978 ◽  
Vol 18 (1) ◽  
pp. 1-5
Author(s):  
H. Homma ◽  
M. Tamura ◽  
K. Watanabe ◽  
K. Sagawa ◽  
K. Inatomi ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Stage ◽  
Cancer Society ◽  
Stage Classification

scholarly journals Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

2020 ◽  
Vol 10 (5) ◽  
pp. 1797 ◽  
Author(s):  
Mera Kartika Delimayanti ◽  
Bedy Purnama ◽  
Ngoc Giang Nguyen ◽  
Mohammad Reza Faisal ◽  
Kunti Robiatul Mahmudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

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%.

Using transfer learning for diabetic retinopathy stage classification

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Enas M.F. El Houby
Keyword(s):  
Diabetic Retinopathy ◽  
Transfer Learning ◽  
Grade Level ◽  
Design Methodology ◽  
Content Type ◽  
Stage Classification ◽  
Set Up ◽  
Retinal Fundus ◽  
Automated Methods

PurposeDiabetic retinopathy (DR) is one of the dangerous complications of diabetes. Its grade level must be tracked to manage its progress and to start the appropriate decision for treatment in time. Effective automated methods for the detection of DR and the classification of its severity stage are necessary to reduce the burden on ophthalmologists and diagnostic contradictions among manual readers.Design/methodology/approachIn this research, convolutional neural network (CNN) was used based on colored retinal fundus images for the detection of DR and classification of its stages. CNN can recognize sophisticated features on the retina and provides an automatic diagnosis. The pre-trained VGG-16 CNN model was applied using a transfer learning (TL) approach to utilize the already learned parameters in the detection.FindingsBy conducting different experiments set up with different severity groupings, the achieved results are promising. The best-achieved accuracies for 2-class, 3-class, 4-class and 5-class classifications are 86.5, 80.5, 63.5 and 73.7, respectively.Originality/valueIn this research, VGG-16 was used to detect and classify DR stages using the TL approach. Different combinations of classes were used in the classification of DR severity stages to illustrate the ability of the model to differentiate between the classes and verify the effect of these changes on the performance of the model.

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