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2024 | OriginalPaper | Buchkapitel

An Analysis of Brain Tumor Segmentation and Classification Techniques of Deep Learning

verfasst von : Amar Saraswat, Shweta Bansal, Anupam Dalal

Erschienen in: Mobile Radio Communications and 5G Networks

Verlag: Springer Nature Singapore

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Abstract

Machine Learning plays a critical role in automating tasks that require human wisdom. Although significant studies have been done on segmenting brain tumors using multiple categories of Magnetic Resonance Images, a human evaluator is still required to bridge the semantic gap between the findings of machine learning approaches and those of the human evaluator. The inconsistencies in the shape of the Brain Tumor, as well as its many types and subtypes with varying behaviors, are the reasons behind this. Various strategies for segmenting a brain tumor and extracting characteristics are deeply analyzed in this paper.

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Vorheriges Kapitel A Predictive Modeling to Assess the Underlying Risks of Stroke

Nächstes Kapitel Assessment of Various MapReduce Scheduling in Heterogeneous Environment

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Titel: An Analysis of Brain Tumor Segmentation and Classification Techniques of Deep Learning
verfasst von: Amar Saraswat
Shweta Bansal
Anupam Dalal
Verlag: Springer Nature Singapore
Buch: Mobile Radio Communications and 5G Networks
Print ISBN: 978-981-9706-99-0

Electronic ISBN: 978-981-9707-00-3

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-97-0700-3_51

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