A Novel Approach to Increase Breast Cancer Chemosensitivity: Disruption of the Anti-Apoptotic Function of Translation Factor eIF4E

2003 ◽  
Author(s):  
Vitaly A. Polunovsky
Keyword(s):  
Breast Cancer ◽  
Translation Factor ◽  
Novel Approach

Targeting Micrornas With Small Molecules: A Novel Approach to Treating Breast Cancer

2010 ◽  
Author(s):  
George A. Calin ◽  
Shuxing Zhang ◽  
Waldemar Priebe
Keyword(s):  
Breast Cancer ◽  
Small Molecules ◽  
Novel Approach

A novel approach using telomerase-specific replication-selective adenovirus for detection of circulating tumor cells in breast cancer patients

2011 ◽  
Vol 128 (3) ◽  
pp. 765-773 ◽  
Author(s):  
Seung Jin Kim ◽  
Akinori Masago ◽  
Yasuhiro Tamaki ◽  
Kenji Akazawa ◽  
Fumine Tsukamoto ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Breast Cancer Patients ◽  
Novel Approach

scholarly journals Identification of Micro-calcification in Mammogram for Breast Cancer Analysis using SVM Classifier

2018 ◽  
Vol 7 (2.16) ◽  
pp. 29
Author(s):  
Gaurav Makwana ◽  
Lalita Gupta
Keyword(s):  
Breast Cancer ◽  
Clustering Algorithm ◽  
Automatic Segmentation ◽  
Breast Cancer Diagnosis ◽  
The State ◽  
Svm Classifier ◽  
Common Disease ◽  
Test Technique ◽  
Novel Approach ◽  
Breast Calcification

Breast cancer is most common disease in women of all ages. To identify & confirm the state of tumor in breast cancer diagnosis, patients are undergo biopsy number of times to identify malignancy. Early detection of cancer can save the patient. In this paper a novel approach for automatic segmentation & classification of breast calcification is proposed. The diagnostic test technique for detection of breast condition is very costly & requires human expertise whereas proposed method can help in automatically identifying the disease by comparing the data with the standard database. In proposed method a database has been created to define various stage of breast calcification & testing images are pre-processed to resize, enhance & filtered to remove background noise. Clustering is performed by using k-means clustering algorithm. GLCM is used to extract out statistical feature like area, mean, variance, standard deviation, homogeneity, skewness etc. to classify the state of tumor. SVM classifier is used for the classification using extracted feature. 

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