High performance classifier for brain tumor detection using capsule neural network

J. S. Thanga Purni; R. Vedhapriyavadhana; S. L. Jayalakshmi; R. Girija

doi:10.1007/978-981-19-7169-3_14

High performance classifier for brain tumor detection using capsule neural network

J. S. Thanga Purni
, R. Vedhapriyavadhana^*
, S. L. Jayalakshmi
, R. Girija

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Downloads (Pure)

Abstract

Brain tumor is a kind of dangerous disease that continues to spread worldwide. Therefore, early diagnosis and treatments are extremely important in this case, and it is considered the most common form of cancer that can be found in children and adults. Finding out the right type of tumor in the early stages is an important part for developing a specific treatment and diagnosing the patient’s response according to the treatment. In this paper, we propose a Caps Net (Capsule Neural Network) model to avoid the rise of mortality among the brain tumor people and to reduce the time required for accurate diagnosis. The proposed method involves MRI images for classifying the different types of tumors and outperforms CNN in accuracy.

Original language	English
Title of host publication	Computer Vision and Machine Intelligence Paradigms for SDGs
Editors	R.J. Kannan, S.M. Thampi, SH. Wang
Publisher	Springer Singapore
Pages	151-164
Number of pages	14
ISBN (Electronic)	9789811971693
ISBN (Print)	9789811971686
DOIs	https://doi.org/10.1007/978-981-19-7169-3_14
Publication status	Published - 1 Jan 2023
Externally published	Yes

Publication series

Name	Lecture Notes in Electrical Engineering
Publisher	Springer
Volume	967
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.1007/978-981-19-7169-3_14

2021 11 02 Purni et al Classifier accepted - for PureAccepted author manuscript, 5.03 MB

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Purni, J. S. T., Vedhapriyavadhana, R., Jayalakshmi, S. L., & Girija, R. (2023). High performance classifier for brain tumor detection using capsule neural network. In R. J. Kannan, S. M. Thampi, & SH. Wang (Eds.), Computer Vision and Machine Intelligence Paradigms for SDGs (pp. 151-164). (Lecture Notes in Electrical Engineering; Vol. 967). Springer Singapore. https://doi.org/10.1007/978-981-19-7169-3_14

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title = "High performance classifier for brain tumor detection using capsule neural network",

abstract = "Brain tumor is a kind of dangerous disease that continues to spread worldwide. Therefore, early diagnosis and treatments are extremely important in this case, and it is considered the most common form of cancer that can be found in children and adults. Finding out the right type of tumor in the early stages is an important part for developing a specific treatment and diagnosing the patient{\textquoteright}s response according to the treatment. In this paper, we propose a Caps Net (Capsule Neural Network) model to avoid the rise of mortality among the brain tumor people and to reduce the time required for accurate diagnosis. The proposed method involves MRI images for classifying the different types of tumors and outperforms CNN in accuracy.",

author = "Purni, \{J. S. Thanga\} and R. Vedhapriyavadhana and Jayalakshmi, \{S. L.\} and R. Girija",

year = "2023",

month = jan,

day = "1",

doi = "10.1007/978-981-19-7169-3\_14",

language = "English",

isbn = "9789811971686",

series = "Lecture Notes in Electrical Engineering",

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editor = "R.J. Kannan and S.M. Thampi and SH. Wang",

booktitle = "Computer Vision and Machine Intelligence Paradigms for SDGs",

address = "Singapore",

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Purni, JST, Vedhapriyavadhana, R, Jayalakshmi, SL & Girija, R 2023, High performance classifier for brain tumor detection using capsule neural network. in RJ Kannan, SM Thampi & SH Wang (eds), Computer Vision and Machine Intelligence Paradigms for SDGs. Lecture Notes in Electrical Engineering, vol. 967, Springer Singapore, pp. 151-164. https://doi.org/10.1007/978-981-19-7169-3_14

High performance classifier for brain tumor detection using capsule neural network. / Purni, J. S. Thanga; Vedhapriyavadhana, R.; Jayalakshmi, S. L. et al.
Computer Vision and Machine Intelligence Paradigms for SDGs. ed. / R.J. Kannan; S.M. Thampi; SH. Wang. Springer Singapore, 2023. p. 151-164 (Lecture Notes in Electrical Engineering; Vol. 967).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - High performance classifier for brain tumor detection using capsule neural network

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PY - 2023/1/1

Y1 - 2023/1/1

N2 - Brain tumor is a kind of dangerous disease that continues to spread worldwide. Therefore, early diagnosis and treatments are extremely important in this case, and it is considered the most common form of cancer that can be found in children and adults. Finding out the right type of tumor in the early stages is an important part for developing a specific treatment and diagnosing the patient’s response according to the treatment. In this paper, we propose a Caps Net (Capsule Neural Network) model to avoid the rise of mortality among the brain tumor people and to reduce the time required for accurate diagnosis. The proposed method involves MRI images for classifying the different types of tumors and outperforms CNN in accuracy.

AB - Brain tumor is a kind of dangerous disease that continues to spread worldwide. Therefore, early diagnosis and treatments are extremely important in this case, and it is considered the most common form of cancer that can be found in children and adults. Finding out the right type of tumor in the early stages is an important part for developing a specific treatment and diagnosing the patient’s response according to the treatment. In this paper, we propose a Caps Net (Capsule Neural Network) model to avoid the rise of mortality among the brain tumor people and to reduce the time required for accurate diagnosis. The proposed method involves MRI images for classifying the different types of tumors and outperforms CNN in accuracy.

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SN - 9789811971686

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BT - Computer Vision and Machine Intelligence Paradigms for SDGs

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A2 - Thampi, S.M.

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PB - Springer Singapore

ER -

High performance classifier for brain tumor detection using capsule neural network

Abstract

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UN SDGs

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