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Automation of Brain Tumor Segmentation Using Deep Learning

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Deep Learning Technologies for the Sustainable Development Goals

Part of the book series: Advanced Technologies and Societal Change ((ATSC))

Abstract

Today also, radiologist analyze the MR images manually based on their experience and knowledge for segmenting the tumor. Use some graphical software to make the report about the presence of the tumor, its size, and other features. Based on this report doctors diagnose the patient, it is the main source for any doctor to start the treatment of the patient. However, as the MRI reports are based on the experience of the radiologist so it is a big challenge to maintain uniformity in the reports generated from the different MR imaging centers. Therefore, automation in this particular field is very much required for better precision and to maintain uniformity in the report. Therefore, doctor can diagnose the patient in much better way. Deep learning playing a vital role in automating the process of brain tumor and other organ segmentation using MR images. Many researchers developed various state-of-art methods to automate the process of brain tumor segmentation in MR images. There are multiple deep learning methods such as stacked auto- encoder, artificial neural network, convolutional neural network, and Unet used for the process of segmenting the medial images, where CNN is the most successful method for segmenting. In this chapter, the importance of automatic brain tumor segmentation approach. CNN and process of convolution, max pooling discussed in detail. Moreover, application of CNN for automatically segmenting brain tumor also discussed with some state-of-art methods.

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Abbreviations

BT:

Brain tumor

BTS:

Brain tumor segmentation

CAD:

Computer aided diagnosis

CNN:

Convolutional neural network

FCL:

Fully connected layer

GAP:

Global average pooling

GMP:

Global max pooling

ML:

Machine learning

MRI:

Medical resonance imaging

SOP:

Sum of product

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Author information

Authors and Affiliations

  1. School of Computer Science, UPES, Dehradun, Uttarakhand, India

    Amit Verma

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  1. Amit Verma
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Correspondence to Amit Verma .

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Editors and Affiliations

  1. University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Virender Kadyan

  2. University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    T. P. Singh

  3. University of Port Harcourt, Port Harcourt, Rivers State, Nigeria

    Chidiebere Ugwu

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Verma, A. (2023). Automation of Brain Tumor Segmentation Using Deep Learning. In: Kadyan, V., Singh, T.P., Ugwu, C. (eds) Deep Learning Technologies for the Sustainable Development Goals. Advanced Technologies and Societal Change. Springer, Singapore. https://doi.org/10.1007/978-981-19-5723-9_13

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  • DOI: https://doi.org/10.1007/978-981-19-5723-9_13

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  • Online ISBN: 978-981-19-5723-9

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