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Deep Learning for Brain Tumor Segmentation

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Deep Learning for Cancer Diagnosis

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

Abstract

Brain tumors are considered to be one of the most lethal types of tumor. Accurate segmentation of brain MRI is an important task for the analysis of neurological diseases. The mortality rate of brain tumors is increasing according to World Health Organization. Detection at early stages of brain tumors can increase the expectation of the patients’ survival. Concerning artificial intelligence approaches for clinical diagnosis of brain tumors, there is an increasing interest in segmentation approaches based on deep learning because of its ability of self-learning over large amounts of data. Deep learning is nowadays a very promising approach to develop effective solution for clinical diagnosis. This chapter provides at first some basic concepts and techniques behind brain tumor segmentation. Then the imaging techniques used for brain tumor visualization are described. Later on, the dataset and segmentation methods are discussed.

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Authors and Affiliations

  1. Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Rome, Italy

    Khushboo Munir, Fabrizio Frezza & Antonello Rizzi

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  1. Khushboo Munir
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  2. Fabrizio Frezza
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Correspondence to Khushboo Munir .

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Editors and Affiliations

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

    Utku Kose

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

    Jafar Alzubi

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Munir, K., Frezza, F., Rizzi, A. (2021). Deep Learning for Brain Tumor Segmentation. 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_11

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