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An Efficient Feed Foreword Network Model with Sine Cosine Algorithm for Breast Cancer Classification

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  • Santosh Kumar Majhi

    (VSS University of Technology, Burla, India)

Abstract

This article describes how breast cancer is the most common invasive cancer in females worldwide and is major cause of deaths. The diagnoses of breast cancer include mammograms, breast ultrasound, magnetic resonance imaging (MRI), ductogram and biopsy. Biopsy is best and only way to know if the breast tumour is cancerous. Reports say that positive detection of breast cancer through biopsy can reach as low as 10%. So many statistical techniques and cognitive science approaches like artificial intelligence are being used to detect the type of breast cancer in a patient. This article presents the breast cancer classification using a feed foreword neural network trained by a sine-cosine algorithm. The superiority of the SCA-NN is shown by experimenting on the Wisconsin Hospital data set and comparing with the recently reported results. The evaluations show that the proposed approach is very robust, effective and gives better correct classification as compared to other classifiers.

Suggested Citation

  • Santosh Kumar Majhi, 2018. "An Efficient Feed Foreword Network Model with Sine Cosine Algorithm for Breast Cancer Classification," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 7(2), pages 1-14, April.
    • Handle: RePEc:igg:jsda00:v:7:y:2018:i:2:p:1-14
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    Cited by:

    1. Anwar E. Ibrahim & Salah Abdel-Mageid & Nadra Nada & Marwa A. Elshahed, 2022. "Human Identification Using Electrocardiogram Signal as a Biometric Trait," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 11(3), pages 1-17, August.
    2. Priya Bhaskar Pandharbale & Sachi Nandan Mohanty & Alok Kumar Jagadev, 2021. "Novel Clustering-Based Web Service Recommendation Framework," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 11(5), pages 1-15, September.
    3. Jiang Wu & Feng Miu & Taiyong Li, 2020. "Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market," Energies, MDPI, vol. 13(7), pages 1-20, April.
    4. Akram Belazi & Héctor Migallón & Daniel Gónzalez-Sánchez & Jorge Gónzalez-García & Antonio Jimeno-Morenilla & José-Luis Sánchez-Romero, 2022. "Enhanced Parallel Sine Cosine Algorithm for Constrained and Unconstrained Optimization," Mathematics, MDPI, vol. 10(7), pages 1-47, April.
    5. Héctor Migallón & Akram Belazi & José-Luis Sánchez-Romero & Héctor Rico & Antonio Jimeno-Morenilla, 2020. "Settings-Free Hybrid Metaheuristic General Optimization Methods," Mathematics, MDPI, vol. 8(7), pages 1-25, July.

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