Automated Brain Tumor Detection Using Soft Computing-Based Segmentation Technique

Muhammad Zubair; Muhammad Umair; Muhammad Owais

doi:10.1109/ICCIT58132.2023.10273963

Automated Brain Tumor Detection Using Soft Computing-Based Segmentation Technique

Muhammad Zubair^*
, Muhammad Umair
, Muhammad Owais

^*Corresponding author for this work

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

18 Scopus citations

Abstract

Development and growth of abnormal cells within the brain results in brain tumor. In this study, a novel segmentation methodology is proposed for the segmentation of tumor. The proposed model consists of two phases. In the first phase, the brain CT image from the medical database is pre-processed to remove artifacts and noise. For Image segmentation, a Hierarchical Self Organizing Map (HSOM) is used that provides promising segmentation results. The conformist Self Organizing Map (SOM), which was used to categorize the picture row by row, is extended by the HSOM. Thus, the HSOM with vector quantization speeds up calculation at this lowest level of the weight vector, where there are more tumor pixels. The proposed automated system is tested on Kaggle (online available) database and achieves an accuracy of 98.94%.

Original language	English
Title of host publication	2023 3rd International Conference on Computing and Information Technology, ICCIT 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	211-215
Number of pages	5
ISBN (Electronic)	9798350321487
DOIs	https://doi.org/10.1109/ICCIT58132.2023.10273963
State	Published - 2023
Externally published	Yes
Event	3rd International Conference on Computing and Information Technology, ICCIT 2023 - Tabuk, Saudi Arabia Duration: 13 Sep 2023 → 14 Sep 2023

Publication series

Name	2023 3rd International Conference on Computing and Information Technology, ICCIT 2023

Conference

Conference	3rd International Conference on Computing and Information Technology, ICCIT 2023
Country/Territory	Saudi Arabia
City	Tabuk
Period	13/09/23 → 14/09/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

CNN
CT scan
MRI
SOM
SVM
artifact
benign
brain tumor
malignant
segmentation

ASJC Scopus subject areas

Electrical and Electronic Engineering
Mechanical Engineering
Energy Engineering and Power Technology

Access to Document

10.1109/ICCIT58132.2023.10273963

Cite this

Zubair, M., Umair, M., & Owais, M. (2023). Automated Brain Tumor Detection Using Soft Computing-Based Segmentation Technique. In 2023 3rd International Conference on Computing and Information Technology, ICCIT 2023 (pp. 211-215). (2023 3rd International Conference on Computing and Information Technology, ICCIT 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCIT58132.2023.10273963

Zubair, Muhammad ; Umair, Muhammad ; Owais, Muhammad. / Automated Brain Tumor Detection Using Soft Computing-Based Segmentation Technique. 2023 3rd International Conference on Computing and Information Technology, ICCIT 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 211-215 (2023 3rd International Conference on Computing and Information Technology, ICCIT 2023).

@inproceedings{07b6c0efb41e41a1a26582dbd19485ad,

title = "Automated Brain Tumor Detection Using Soft Computing-Based Segmentation Technique",

abstract = "Development and growth of abnormal cells within the brain results in brain tumor. In this study, a novel segmentation methodology is proposed for the segmentation of tumor. The proposed model consists of two phases. In the first phase, the brain CT image from the medical database is pre-processed to remove artifacts and noise. For Image segmentation, a Hierarchical Self Organizing Map (HSOM) is used that provides promising segmentation results. The conformist Self Organizing Map (SOM), which was used to categorize the picture row by row, is extended by the HSOM. Thus, the HSOM with vector quantization speeds up calculation at this lowest level of the weight vector, where there are more tumor pixels. The proposed automated system is tested on Kaggle (online available) database and achieves an accuracy of 98.94\%.",

keywords = "CNN, CT scan, MRI, SOM, SVM, artifact, benign, brain tumor, malignant, segmentation",

author = "Muhammad Zubair and Muhammad Umair and Muhammad Owais",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 3rd International Conference on Computing and Information Technology, ICCIT 2023 ; Conference date: 13-09-2023 Through 14-09-2023",

year = "2023",

doi = "10.1109/ICCIT58132.2023.10273963",

language = "English",

series = "2023 3rd International Conference on Computing and Information Technology, ICCIT 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "211--215",

booktitle = "2023 3rd International Conference on Computing and Information Technology, ICCIT 2023",

address = "United States",

}

Zubair, M, Umair, M & Owais, M 2023, Automated Brain Tumor Detection Using Soft Computing-Based Segmentation Technique. in 2023 3rd International Conference on Computing and Information Technology, ICCIT 2023. 2023 3rd International Conference on Computing and Information Technology, ICCIT 2023, Institute of Electrical and Electronics Engineers Inc., pp. 211-215, 3rd International Conference on Computing and Information Technology, ICCIT 2023, Tabuk, Saudi Arabia, 13/09/23. https://doi.org/10.1109/ICCIT58132.2023.10273963

Automated Brain Tumor Detection Using Soft Computing-Based Segmentation Technique. / Zubair, Muhammad; Umair, Muhammad; Owais, Muhammad.
2023 3rd International Conference on Computing and Information Technology, ICCIT 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 211-215 (2023 3rd International Conference on Computing and Information Technology, ICCIT 2023).

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

TY - GEN

T1 - Automated Brain Tumor Detection Using Soft Computing-Based Segmentation Technique

AU - Zubair, Muhammad

AU - Umair, Muhammad

AU - Owais, Muhammad

PY - 2023

Y1 - 2023

N2 - Development and growth of abnormal cells within the brain results in brain tumor. In this study, a novel segmentation methodology is proposed for the segmentation of tumor. The proposed model consists of two phases. In the first phase, the brain CT image from the medical database is pre-processed to remove artifacts and noise. For Image segmentation, a Hierarchical Self Organizing Map (HSOM) is used that provides promising segmentation results. The conformist Self Organizing Map (SOM), which was used to categorize the picture row by row, is extended by the HSOM. Thus, the HSOM with vector quantization speeds up calculation at this lowest level of the weight vector, where there are more tumor pixels. The proposed automated system is tested on Kaggle (online available) database and achieves an accuracy of 98.94%.

AB - Development and growth of abnormal cells within the brain results in brain tumor. In this study, a novel segmentation methodology is proposed for the segmentation of tumor. The proposed model consists of two phases. In the first phase, the brain CT image from the medical database is pre-processed to remove artifacts and noise. For Image segmentation, a Hierarchical Self Organizing Map (HSOM) is used that provides promising segmentation results. The conformist Self Organizing Map (SOM), which was used to categorize the picture row by row, is extended by the HSOM. Thus, the HSOM with vector quantization speeds up calculation at this lowest level of the weight vector, where there are more tumor pixels. The proposed automated system is tested on Kaggle (online available) database and achieves an accuracy of 98.94%.

KW - CNN

KW - CT scan

KW - MRI

KW - SOM

KW - SVM

KW - artifact

KW - benign

KW - brain tumor

KW - malignant

KW - segmentation

UR - https://www.scopus.com/pages/publications/85175437626

U2 - 10.1109/ICCIT58132.2023.10273963

DO - 10.1109/ICCIT58132.2023.10273963

M3 - Conference contribution

AN - SCOPUS:85175437626

T3 - 2023 3rd International Conference on Computing and Information Technology, ICCIT 2023

SP - 211

EP - 215

BT - 2023 3rd International Conference on Computing and Information Technology, ICCIT 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd International Conference on Computing and Information Technology, ICCIT 2023

Y2 - 13 September 2023 through 14 September 2023

ER -

Zubair M, Umair M, Owais M. Automated Brain Tumor Detection Using Soft Computing-Based Segmentation Technique. In 2023 3rd International Conference on Computing and Information Technology, ICCIT 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 211-215. (2023 3rd International Conference on Computing and Information Technology, ICCIT 2023). doi: 10.1109/ICCIT58132.2023.10273963

Automated Brain Tumor Detection Using Soft Computing-Based Segmentation Technique

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this