A hybrid artificial bee colony with whale optimization algorithm for improved breast cancer diagnosis

2021 ◽  
Author(s):  
Punitha Stephan ◽  
Thompson Stephan ◽  
Ramani Kannan ◽  
Ajith Abraham
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Optimization Algorithm ◽  
Artificial Bee Colony ◽  
Breast Cancer Diagnosis ◽  
Whale Optimization Algorithm ◽  
Bee Colony ◽  
Whale Optimization

An Intelligent Artificial Bee Colony and Adaptive Bacterial Foraging Optimization Scheme for reliable breast cancer diagnosis

2020 ◽  
Vol 13 ◽  
Author(s):  
S. Punitha ◽  
A. Amuthan ◽  
K. Suresh Joseph
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Cancer Diagnosis ◽  
Artificial Bee Colony ◽  
Breast Cancer Diagnosis ◽  
Bacterial Foraging Optimization ◽  
Data Set ◽  
Cancer Data ◽  
Bacterial Foraging ◽  
Bee Colony

: Breast cancer is essential to be detected in primitive localized stage for enhancing the possibility of survival since it is considered as the major malediction to the women society around the globe. Most of the intelligent approaches devised for breast cancer necessitates expertise that results in reliable identification of patterns that conclude the presence of oncology cells and determine the possible treatment to the breast cancer patients in order to enhance their survival feasibility. Moreover, the majority of the existing scheme of the literature incurs intensive labor and time, which induces predominant impact over the diagnosis time utilized for detecting breast cancer cells. An Intelligent Artificial Bee Colony and Adaptive Bacterial Foraging Optimization (IABC-ABFO) scheme is proposed for facilitating better rate of local and global searching ability in selecting the optimal features subsets and optimal parameters of ANN considered for breast cancer diagnosis. In the proposed IABC-ABFO approach, the traditional ABC algorithm used for cancer detection is improved by integrating an adaptive bacterial foraging process in the onlooker bee and the employee bee phase that results in an optimal exploitation and exploration. The results investigation of the proposed IABC-ABFO approach facilitated using Wisconsin breast cancer data set confirmed an enhanced mean classification accuracy of 99.52% on par with the existing baseline cancer detection schemes.

Ductoscope Lights Up Breast Cancer Diagnosis

2007 ◽  
Vol 40 (14) ◽  
pp. 32
Author(s):  
MICHELE G. SULLIVAN
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis

Physical Activity and Survival Subsequent to Metastatic Breast Cancer Diagnosis

2010 ◽  
Author(s):  
Susan Sharp ◽  
Ashleigh Golden ◽  
Cheryl Koopman ◽  
Eric Neri ◽  
David Spiegel
Keyword(s):  
Breast Cancer ◽  
Physical Activity ◽  
Metastatic Breast Cancer ◽  
Cancer Diagnosis ◽  
Metastatic Breast ◽  
Breast Cancer Diagnosis
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