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Enhanced Monarchy Butterfly Optimization Technique for effective breast cancer diagnosis

  • Patient Facing Systems
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Abstract

Breast cancer is the biggest curse for the women society in the world since the survival factor of the infected patients is ensured only when it is detected at the early localized stage. The majority of the intelligent schemes proposed for detecting the breast cancer relies on the human skill that helps in trustworthy determination of essential pattern that confirms the existence of the infected cancer cells for deciding upon the course of treatment. Further, most of the research works contributed in the literature for detecting breast cancer necessitates huge time and laborinvolved that increases the time of diagnosis. This Intelligent Artificial Bee Colony and Enhanced Monarchy Butterfly Optimization Technique (IABC-EMBOT) is proposed for effective breast cancer diagnosis. The core idea behind the formulation of IABC-EMBOT relies on two significant ameliorations that, i) focuses on the modification of Monarchy Butterfly Optimization that enhances the exploration degree based on the rate of exploitation of the searching space and ii) concentrates on the elimination in the limitations of the ABC scheme by enhancing the possibility of search diversification process through phenomenal update facilitated through the dynamic and adaptive butterfly operator that improves the search globally. The proposed IABC-EMBOT scheme investigated using the Wisconsin data set is proven to facilitate an improved average classification accuracy of 97.53%.

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Authors and Affiliations

  1. Department of Computer Science, Pondicherry University, Pondicherry, India

    S. Punitha & K. Suresh Joseph

  2. Department of Computer Science Engineering, Pondicherry Engineering College, Pondicherry, India

    A. Amuthan

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  1. S. Punitha
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  2. A. Amuthan
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Correspondence to S. Punitha.

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Punitha, S., Amuthan, A. & Joseph, K.S. Enhanced Monarchy Butterfly Optimization Technique for effective breast cancer diagnosis. J Med Syst 43, 206 (2019). https://doi.org/10.1007/s10916-019-1348-8

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