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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.
A Novel Deep Learning-based Whale Optimization Algorithm for Prediction of Breast Cancer