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Predicting the Cancer Recurrence Using Artificial Neural Networks

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Computational Intelligence in Oncology

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1016))

Abstract

In the early 1900s, multiple significant studies showed high incidences of cancer. During this period, study with infectious agents produced only modest results which looked irrelevant to people. Then, in the 1980s, groundbreaking evidence that a number of viruses can cause cancer in people began to emerge. Machine learning and deep learning techniques have been widely employed in cancer detection and classification that include support vector machines (SVMs), artificial neural networks (ANNs), and conventional neural networks (CNNs). The recurrence of cancer is also an important issue that needs to be predicted with significant accuracy. This chapter reviews current state-of-the-art of ANNs model in the prediction of cancer recurrence.

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Author information

Authors and Affiliations

  1. Bioinformatics Program, Faculty of Computer Science and Information Technology, Ain Shams University, Cairo, Egypt

    Mohamed Soudy

  2. Department of Computer Application, Integral University, Lucknow, India

    Afroz Alam

  3. Maulana Mazharul Haque Arabic and Persian University, Patna, India

    Obiadul Ola

Authors
  1. Mohamed Soudy
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  2. Afroz Alam
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  3. Obiadul Ola
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Editors and Affiliations

  1. Department of Computer Science, Jamia Millia Islamia, New Delhi, India

    Khalid Raza

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Soudy, M., Alam, A., Ola, O. (2022). Predicting the Cancer Recurrence Using Artificial Neural Networks. In: Raza, K. (eds) Computational Intelligence in Oncology. Studies in Computational Intelligence, vol 1016. Springer, Singapore. https://doi.org/10.1007/978-981-16-9221-5_10

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