Extreme Learning Bat Algorithm in Brain Tumor Classification
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Extreme Learning Bat Algorithm in Brain Tumor Classification
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Extreme Learning Bat Algorithm in Brain Tumor Classification
Popis výsledku anglicky
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.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
—
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2022
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Intelligent Automation & Soft Computing: An International Journal
ISSN
1079-8587
e-ISSN
2326-005X
Svazek periodika
34
Číslo periodika v rámci svazku
1
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
17
Strana od-do
249-265
Kód UT WoS článku
000791404800002
EID výsledku v databázi Scopus
2-s2.0-85129091151