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Future of Deep Learning for Cancer Diagnosis

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Deep Learning for Cancer Diagnosis

Part of the book series: Studies in Computational Intelligence ((SCI,volume 908))

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Abstract

Long-term treatments observed in cancer-derived diseases have a higher risk of death than other diseases. The effectiveness of the developing technology in human life is manifested in the treatment and diagnosis of diseases. ‘Early diagnosis saves lives’, which is frequently heard in all diseases, comes to life in this part. The first of the most important points in cancer and derivative diseases is early detection of the disease. Artificial intelligence is used to simulate and simplify the human life offered by developing technology. This study focuses on the methods of deep learning, which is one of the subfields of artificial intelligence. The aim of this study is to emphasize the deep learning methods used in cancer diagnosis. As a result of emphasizing the methods, the present and future potential of the literature in terms of cancer diagnosis has been revealed. It is thought that the study will be a current reference for the researchers who will conduct research within the scope of the subject.

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Author information

Authors and Affiliations

  1. Institute of Education Sciences, Computer and Instructional Technologies Education, Suleyman Demirel University, Isparta, Turkey

    Pinar Koc

  2. Institute of Education Sciences, Computer Engineering Department, Suleyman Demirel University, Isparta, Turkey

    Cihan Yalcin

Authors
  1. Pinar Koc
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  2. Cihan Yalcin
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Correspondence to Cihan Yalcin .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Süleyman Demirel University, Isparta, Turkey

    Utku Kose

  2. Faculty of Engineering, Al-Balqa Applied University, Balqa, Jordan

    Jafar Alzubi

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Koc, P., Yalcin, C. (2021). Future of Deep Learning for Cancer Diagnosis. In: Kose, U., Alzubi, J. (eds) Deep Learning for Cancer Diagnosis. Studies in Computational Intelligence, vol 908. Springer, Singapore. https://doi.org/10.1007/978-981-15-6321-8_13

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