Extreme Learning Bat Algorithm in Brain Tumor Classification
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50019407" target="_blank" >RIV/62690094:18470/22:50019407 - isvavai.cz</a>
Result on the web
<a href="https://www.techscience.com/iasc/v34n1/47351" target="_blank" >https://www.techscience.com/iasc/v34n1/47351</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.32604/iasc.2022.024538" target="_blank" >10.32604/iasc.2022.024538</a>
Alternative languages
Result language
angličtina
Original language name
Extreme Learning Bat Algorithm in Brain Tumor Classification
Original language description
Brain tumor is considered as an unusual cell that presents and grows in the brain. Similarly, it may lead to cancerous or non-cancerous. So, to improve the survival rate of the patient and to give the best treatment at the earliest, it's very necessary for early prediction of tumor. Accurate classification of tumor in the brain is important for improving the diagnosis. In accordance with that, various research programs are invited for the better treatment of the patients. Machine Learning (ML) algorithms are applied to help the health associates for the classification of brain tumor and present their diagnosis. This paper focuses primarily on brain tumors of meningioma, Glioma, and pituitary. Moreover, the manual evaluation of Magnetic Resonance Image (MRI) is a difficult process. For accessing MRI brain image in the aspects of its volume, boundaries, detecting tumor size, shape and classification are the challenging tasks. To overcome these difficulties, this paper proposes a novel approach in feature selection using bat algorithm with Extreme Learning Machine (ELM) and for enhancing the accurate classification by Transfer Learning (BA + ELM-TL). Here the data is pre-processed to remove noises; Stationary Wavelet Transforms (SWT) is used to extract the features from the MRI brain image. This paper has collected the dataset from fig share, whole brain atlas and TCGA-GBM data set. Therefore, it is proved that 92.6% is the accuracy of Bat algorithm, 90.4% for Extreme Learning algorithm and 98.87% for BA + ELM-TL.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Intelligent Automation & Soft Computing: An International Journal
ISSN
1079-8587
e-ISSN
2326-005X
Volume of the periodical
34
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
17
Pages from-to
249-265
UT code for WoS article
000791404800002
EID of the result in the Scopus database
2-s2.0-85129091151