Role of normalization of breast thermogram images and automatic classification of breast cancer

2017 ◽  
Vol 35 (1) ◽  
pp. 57-70 ◽  
Author(s):  
Dayakshini Sathish ◽  
Surekha Kamath ◽  
Keerthana Prasad ◽  
Rajagopal Kadavigere
Keyword(s):  
Breast Cancer ◽  
Automatic Classification

scholarly journals Fully automatic classification of breast cancer microarray images

2016 ◽  
Vol 3 (2) ◽  
pp. 348-359 ◽  
Author(s):  
Nastaran Dehghan Khalilabad ◽  
Hamid Hassanpour ◽  
Mohammad Reza Abbaszadegan
Keyword(s):  
Breast Cancer ◽  
Automatic Classification ◽  
Fully Automatic

scholarly journals A methodology for automatic classification of breast cancer immunohistochemical data using semi-supervised Fuzzy c-means

2013 ◽  
Vol 22 (3) ◽  
pp. 475-499 ◽  
Author(s):  
Daphne Teck Ching Lai ◽  
Jonathan M. Garibaldi ◽  
Daniele Soria ◽  
Christopher M. Roadknight
Keyword(s):  
Breast Cancer ◽  
Automatic Classification ◽  
Fuzzy C Means

The role of core biopsy in the preoperative classification of breast cancer according to prognostic factors

2013 ◽  
Vol 85 (1) ◽  
pp. 1-7
Author(s):  
S. Lanitis ◽  
P. Lazari ◽  
Ch. Kontovounisios ◽  
Ch. Karaliotas ◽  
G. Sgourakis ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
Core Biopsy

Breast Cancer Classification Using Nanochannel Arrays

2012 ◽  
Vol 1468 ◽  
Author(s):  
Kelly Flanagan ◽  
Krishna Vattipalli ◽  
Anjan Panneer Selvam ◽  
Shalini Prasad
Keyword(s):  
Breast Cancer ◽  
Electrochemical Impedance ◽  
Cellular Level ◽  
Cancer Diagnostics ◽  
Non Invasive ◽  
Cell Lysate ◽  
Disease Initiation ◽  
Nanochannel Arrays

ABSTRACTThe ability to design a diagnostics platform that can achieve cellular level as well as molecular level classification of targeted biomarkers may be critical toward understanding the fundamental basis of disease initiation and proliferation in breast cancer. In this context, we have looked at breast cancer diagnostics and present the design of a biomedical microdevice for evaluating and classifying cellular samples based on their risk towards metastasis. Primary breast cancer tumors have been shown to contain heterogeneous populations of neoplastic cells. Recent studies have demonstrated that subpopulations of these cells can cooperate in the initiation of collective invasion and metastasis. The role of the sensor we present is to identify the type of cells as non-invasive/”follower” cells that do not result in metastasis or invasive “leader” cells that are thought to be responsible for metastasis, from breast cancer cell lysate samples, thus enabling more selective classification of samples, with the eventual goal of early diagnosis. The device is an electrical immunoassay that incorporates the PDGF- receptor to screen the cell lysate samples for the PGDF binding protein that is preferentially expressed in the invasive, “leader” cells. The sensor comprises of alumina nanochannel arrays integrated on to a microelectronic platform operating on the principle of electrochemical impedance spectroscopy to quantify the PGDF protein from the cell lysates.

Automatic classification of breast cancer histopathological images based on deep feature fusion and enhanced routing

2021 ◽  
Vol 65 ◽  
pp. 102341
Author(s):  
Pin Wang ◽  
Jiaxin Wang ◽  
Yongming Li ◽  
Pufei Li ◽  
Linyu Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Feature Fusion ◽  
Automatic Classification ◽  
Deep Feature ◽  
Histopathological Images

scholarly journals Formulation of probability-based pervasive information set features and Hanman transform classifier for the categorization of mammograms

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
Jyoti Dabass ◽  
M. Hanmandlu ◽  
Rekha Vig
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Statistical Analysis ◽  
Fuzzy Set ◽  
State Of The Art ◽  
Entropy Function ◽  
Information Set ◽  
Learning Architectures

AbstractWith aim of detecting breast cancer at the early stages using mammograms, this study presents the formulation of five feature types by extending the information set to encompass the concept of an intuitionist fuzzy set. The resulting pervasive information set gives not only the certainty of the pixel intensities of mammograms to a class but also the deficiency in the fuzzy modeling referred to as the hesitancy. The generalized adaptive Hanman Anirban fuzzy entropy function is shown to be equivalent to the hesitancy entropy function. The probability-based fuzzy Hanman transform and the pervasive Information with probability taking the role of hesitancy degree help derive the above five feature types termed as probability-based pervasive Information set features. The effectiveness of each feature type is demonstrated on the mini-MIAS and DDSM databases for the multi-class categorization of mammograms using the Hanman transform classifier. The statistical analysis by ANOVA test proves that the features are statistically significant and the experimental results are shown to be clinically relevant by the expert radiologists. The performance of the five feature types is either superior to or equal to that of some deep learning architectures on comparison but they outperform the state-of-the-art literature methods in the classification of breast cancer using mammograms.

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