A Study on Nuclei Shape Features at the Classification of Glioma Disease Stage Using CNN

2020 ◽  
Vol 140 (12) ◽  
pp. 1367-1368
Author(s):  
Daisuke Saito ◽  
Hiroharu Kawanaka ◽  
V. B. Surya Prasath ◽  
Bruce J. Aronow
Keyword(s):  
Disease Stage ◽  
Shape Features

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

2020 ◽  
Vol 13 (5) ◽  
pp. 508-523 ◽  
Author(s):  
Guan‐Hua Huang ◽  
Chih‐Hsuan Lin ◽  
Yu‐Ren Cai ◽  
Tai‐Been Chen ◽  
Shih‐Yen Hsu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Stage ◽  
Brain Images ◽  
Functional Brain ◽  
Machine Learning Classification

scholarly journals ICD-11 extension codes support detailed clinical abstraction and comprehensive classification

2021 ◽  
Vol 21 (S6) ◽  
Author(s):  
Saskia E. Drösler ◽  
Stefanie Weber ◽  
Christopher G. Chute
Keyword(s):  
Clinical History ◽  
International Classification Of Diseases ◽  
Disease Stage ◽  
Clinical State ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Clinical Detail ◽  
History Of ◽  
Comprehensive Classification

Abstract Background The new International Classification of Diseases—11th revision (ICD-11) succeeds ICD-10. In the three decades since ICD-10 was released, demands for detailed information on the clinical history of a morbid patient have increased. Methods ICD-11 has now implemented an addendum chapter X called “Extension Codes”. This chapter contains numerous codes containing information on concepts including disease stage, severity, histopathology, medicaments, and anatomical details. When linked to a stem code representing a clinical state, the extension codes add significant detail and allow for multidimensional coding. Results This paper discusses the purposes and uses of extension codes and presents three examples of how extension codes can be used in coding clinical detail. Conclusion ICD-11 with its extension codes implemented has the potential to improve precision and evidence based health care worldwide.

Classification of lung nodules in diagnostic CT: an approach based on 3D vascular features, nodule density distribution, and shape features

2003 ◽  
Author(s):  
Shih-Chung B. Lo ◽  
Li-Yueh Hsu ◽  
Matthew T. Freedman ◽  
Yuan Ming F. Lure ◽  
Hui Zhao
Keyword(s):  
Density Distribution ◽  
Lung Nodules ◽  
Shape Features ◽  
Diagnostic Ct

scholarly journals Classification of Hyperspectral Reflectance Images With Physical and Statistical Criteria

2020 ◽  
Vol 12 (14) ◽  
pp. 2335 ◽  
Author(s):  
Alexandre Alakian ◽  
Véronique Achard
Keyword(s):  
Classification Algorithms ◽  
Type Ii ◽  
Spectral Indices ◽  
Shape Features ◽  
Hyperspectral Reflectance ◽  
Roof Tile ◽  
Dark Green ◽  
Number Of Classes ◽  
Statistical Criteria

A classification method of hyperspectral reflectance images named CHRIPS (Classification of Hyperspectral Reflectance Images with Physical and Statistical criteria) is presented. This method aims at classifying each pixel from a given set of thirteen classes: unidentified dark surface, water, plastic matter, carbonate, clay, vegetation (dark green, dense green, sparse green, stressed), house roof/tile, asphalt, vehicle/paint/metal surface and non-carbonated gravel. Each class is characterized by physical criteria (detection of specific absorptions or shape features) or statistical criteria (use of dedicated spectral indices) over spectral reflectance. CHRIPS input is a hyperspectral reflectance image covering the spectral range [400–2500 nm]. The presented method has four advantages, namely: (i) is robust in transfer, class identification is based on criteria that are not very sensitive to sensor type; (ii) does not require training, criteria are pre-defined; (iii) includes a reject class, this class reduces misclassifications; (iv) high precision and recall, F 1 score is generally above 0.9 in our test. As the number of classes is limited, CHRIPS could be used in combination with other classification algorithms able to process the reject class in order to decrease the number of unclassified pixels.

Machine learning for cultivar classification of apricots (Prunus armeniaca L.) based on shape features

2019 ◽  
Vol 256 ◽  
pp. 108524 ◽  
Author(s):  
Xi Yang ◽  
Ruoyu Zhang ◽  
Zhiqiang Zhai ◽  
Yujie Pang ◽  
Zuohui Jin
Keyword(s):  
Machine Learning ◽  
Prunus Armeniaca ◽  
Shape Features ◽  
Cultivar Classification ◽  
Prunus Armeniaca L

Classification of Upper Limb Motion Trajectories Using Shape Features

2012 ◽  
Vol 42 (6) ◽  
pp. 970-982 ◽  
Author(s):  
Huiyu Zhou ◽  
Huosheng Hu ◽  
Honghai Liu ◽  
Jinshan Tang
Keyword(s):  
Upper Limb ◽  
Shape Features ◽  
Motion Trajectories

SVM classification of patients with Alzheimer's disease and mild cognitive impairment using hippocampal shape features

NeuroImage ◽  
2009 ◽  
Vol 47 ◽  
pp. S57 ◽  
Author(s):  
E Gerardin ◽  
M Chupin ◽  
R Cuingnet ◽  
B Dubois ◽  
S Lehéricy ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Shape Features ◽  
Svm Classification

Automated classification of wear particles based on their surface texture and shape features

2008 ◽  
Vol 41 (1) ◽  
pp. 34-43 ◽  
Author(s):  
Gwidon P. Stachowiak ◽  
Gwidon W. Stachowiak ◽  
Pawel Podsiadlo
Keyword(s):  
Surface Texture ◽  
Wear Particles ◽  
Automated Classification ◽  
Shape Features
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