scholarly journals Measurement based Human Brain Tumor Recognition by Adapting Support Vector Machine

2013 ◽  
Vol 03 (09) ◽  
pp. 26-31 ◽  
Author(s):  
Chandrakant Biradar
PLoS ONE ◽  
2011 ◽  
Vol 6 (11) ◽  
pp. e27442 ◽  
Author(s):  
Ana Gonzalez-Segura ◽  
Jose Manuel Morales ◽  
Jose Manuel Gonzalez-Darder ◽  
Ramon Cardona-Marsal ◽  
Concepcion Lopez-Gines ◽  
...  

1994 ◽  
Vol 81 (1) ◽  
pp. 69-77 ◽  
Author(s):  
Takao Nakagawa ◽  
Toshihiko Kubota ◽  
Masanori Kabuto ◽  
Kazufumi Sato ◽  
Hirokazu Kawano ◽  
...  

✓ The role of matrix metalloproteinases (MMP's) and their inhibitor, tissue inhibitor of metalloproteinases-1 (TIMP-1), in human brain tumor invasion was investigated. Gelatinolytic activity was assayed via gelatin zymography, and four MMP's (MMP-1, MMP-2, MMP-3, and MMP-9) and TIMP-1 were immunolocalized in human brain tumors and in normal brain tissues using monoclonal antibodies. The tissue was surgically removed from 44 patients: glioblastoma (five cases), anaplastic astrocytoma (six cases), astrocytoma (four cases), metastatic tumor (six cases), neurinoma (10 cases), meningioma (10 cases), and normal brain tissue (three cases). Glioblastomas, anaplastic astrocytomas, and metastatic tumors showed high gelatinolytic activity and positive immunostaining for MMP's; TIMP-1 was also expressed in these tumors, but some tumor cells were negative for the antibody. Astrocytomas had low gelatinolytic activity and the tumor cells showed no immunoreactivity for MMP's and TIMP-1. Although neurinomas and meningiomas had only moderate proteinase activity and exhibited positive immunoreactivity for MMP-9, intense expression of TIMP-1 was simultaneously observed in these tumor cells. These findings suggest that MMP's play an important role in human brain tumor invasion, probably due to an imbalance between the production of MMP's and TIMP-1 by the tumor cells.


2016 ◽  
Vol 18 (9) ◽  
pp. 1209-1218 ◽  
Author(s):  
Amy K. LeBlanc ◽  
Christina Mazcko ◽  
Diane E. Brown ◽  
Jennifer W. Koehler ◽  
Andrew D. Miller ◽  
...  

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Susobhan Sarkar ◽  
Candice C. Poon ◽  
Reza Mirzaei ◽  
Khalil S. Rawji ◽  
Walter Hader ◽  
...  

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4487 ◽  
Author(s):  
Himar Fabelo ◽  
Samuel Ortega ◽  
Elizabeth Casselden ◽  
Jane Loh ◽  
Harry Bulstrode ◽  
...  

The work presented in this paper is focused on the use of spectroscopy to identify the type of tissue of human brain samples employing support vector machine classifiers. Two different spectrometers were used to acquire infrared spectroscopic signatures in the wavenumber range between 1200–3500 cm−1. An extensive analysis was performed to find the optimal configuration for a support vector machine classifier and determine the most relevant regions of the spectra for this particular application. The results demonstrate that the developed algorithm is robust enough to classify the infrared spectroscopic data of human brain tissue at three different discrimination levels.


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