Investigations on Brain Tumor Classification Using Hybrid Machine Learning Algorithms
The imaging modalities are used to view other organs and analyze different tissues in the body. In such imaging modalities, a new and developing imaging technique is hyperspectral imaging. This multicolour representation of tissues helps us to better understand the issues compared to the previous im...
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| Published in | Journal of healthcare engineering Vol. 2022; pp. 1 - 9 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Hindawi
14.02.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2040-2295 2040-2309 2040-2309 |
| DOI | 10.1155/2022/2761847 |
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| Abstract | The imaging modalities are used to view other organs and analyze different tissues in the body. In such imaging modalities, a new and developing imaging technique is hyperspectral imaging. This multicolour representation of tissues helps us to better understand the issues compared to the previous image models. This research aims to analyze the tumor localization in the brain by performing different operations on hyperspectral images. The tumor is located using the combination of k-based clustering processes like k-nearest neighbour and k-means clustering. The value of k in both methods is determined using the optimization process called the firefly algorithm. The optimization processes reduce the manual calculation for finding K’s optimal value to segment the brain regions. The labelling of the areas of the brain is done using the multilayer feedforward neural network. The proposed technique produced better results than the existing methods like hybrid k-means clustering and parallel k-means clustering by having a higher peak signal-to-noise ratio and a lesser mean absolute error value. The proposed model achieved 96.47% accuracy, 96.32% sensitivity, and 98.24% specificity, which are improved compared to other techniques. |
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| AbstractList | The imaging modalities are used to view other organs and analyze different tissues in the body. In such imaging modalities, a new and developing imaging technique is hyperspectral imaging. This multicolour representation of tissues helps us to better understand the issues compared to the previous image models. This research aims to analyze the tumor localization in the brain by performing different operations on hyperspectral images. The tumor is located using the combination of k-based clustering processes like k-nearest neighbour and k-means clustering. The value of k in both methods is determined using the optimization process called the firefly algorithm. The optimization processes reduce the manual calculation for finding K's optimal value to segment the brain regions. The labelling of the areas of the brain is done using the multilayer feedforward neural network. The proposed technique produced better results than the existing methods like hybrid k-means clustering and parallel k-means clustering by having a higher peak signal-to-noise ratio and a lesser mean absolute error value. The proposed model achieved 96.47% accuracy, 96.32% sensitivity, and 98.24% specificity, which are improved compared to other techniques. The imaging modalities are used to view other organs and analyze different tissues in the body. In such imaging modalities, a new and developing imaging technique is hyperspectral imaging. This multicolour representation of tissues helps us to better understand the issues compared to the previous image models. This research aims to analyze the tumor localization in the brain by performing different operations on hyperspectral images. The tumor is located using the combination of -based clustering processes like -nearest neighbour and -means clustering. The value of in both methods is determined using the optimization process called the firefly algorithm. The optimization processes reduce the manual calculation for finding 's optimal value to segment the brain regions. The labelling of the areas of the brain is done using the multilayer feedforward neural network. The proposed technique produced better results than the existing methods like hybrid -means clustering and parallel -means clustering by having a higher peak signal-to-noise ratio and a lesser mean absolute error value. The proposed model achieved 96.47% accuracy, 96.32% sensitivity, and 98.24% specificity, which are improved compared to other techniques. The imaging modalities are used to view other organs and analyze different tissues in the body. In such imaging modalities, a new and developing imaging technique is hyperspectral imaging. This multicolour representation of tissues helps us to better understand the issues compared to the previous image models. This research aims to analyze the tumor localization in the brain by performing different operations on hyperspectral images. The tumor is located using the combination of k-based clustering processes like k-nearest neighbour and k-means clustering. The value of k in both methods is determined using the optimization process called the firefly algorithm. The optimization processes reduce the manual calculation for finding K's optimal value to segment the brain regions. The labelling of the areas of the brain is done using the multilayer feedforward neural network. The proposed technique produced better results than the existing methods like hybrid k-means clustering and parallel k-means clustering by having a higher peak signal-to-noise ratio and a lesser mean absolute error value. The proposed model achieved 96.47% accuracy, 96.32% sensitivity, and 98.24% specificity, which are improved compared to other techniques.The imaging modalities are used to view other organs and analyze different tissues in the body. In such imaging modalities, a new and developing imaging technique is hyperspectral imaging. This multicolour representation of tissues helps us to better understand the issues compared to the previous image models. This research aims to analyze the tumor localization in the brain by performing different operations on hyperspectral images. The tumor is located using the combination of k-based clustering processes like k-nearest neighbour and k-means clustering. The value of k in both methods is determined using the optimization process called the firefly algorithm. The optimization processes reduce the manual calculation for finding K's optimal value to segment the brain regions. The labelling of the areas of the brain is done using the multilayer feedforward neural network. The proposed technique produced better results than the existing methods like hybrid k-means clustering and parallel k-means clustering by having a higher peak signal-to-noise ratio and a lesser mean absolute error value. The proposed model achieved 96.47% accuracy, 96.32% sensitivity, and 98.24% specificity, which are improved compared to other techniques. |
| Author | Vijay, A. Rajendran, T. Sridhar, S. Arthi, B. Sherubha, P. Rinesh, S. Waji, Yosef Asrat Maheswari, K. |
| AuthorAffiliation | 2 Department of Computer Science and Engineering, CMR Technical Campus, Hyderabad, Telangana, India 7 Makeit Technologies (Center for Industrial Research), Coimbatore, Tamil Nadu, India 3 Department of Computer Science and Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar,Kattankulathur,Kanchipuram, Chennai, Tamil Nadu, India 4 Department of Information Technology, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India 5 Department of Business Administration and Information Systems, Arba Minch University, Sawla Campus, Ethiopia 1 Department of Computer Science and Engineering, Jigjiga University, Jijiga, Ethiopia 8 Department of Chemical Engineering, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University,Addis Ababa, Ethiopia 6 Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India |
| AuthorAffiliation_xml | – name: 1 Department of Computer Science and Engineering, Jigjiga University, Jijiga, Ethiopia – name: 2 Department of Computer Science and Engineering, CMR Technical Campus, Hyderabad, Telangana, India – name: 3 Department of Computer Science and Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar,Kattankulathur,Kanchipuram, Chennai, Tamil Nadu, India – name: 5 Department of Business Administration and Information Systems, Arba Minch University, Sawla Campus, Ethiopia – name: 8 Department of Chemical Engineering, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University,Addis Ababa, Ethiopia – name: 6 Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India – name: 7 Makeit Technologies (Center for Industrial Research), Coimbatore, Tamil Nadu, India – name: 4 Department of Information Technology, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35198132$$D View this record in MEDLINE/PubMed |
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| References | 22 23 24 25 26 27 28 C. Cortes (17) 2016 10 11 12 13 14 16 18 19 1 2 3 4 5 6 7 8 9 R. Harithaa (15) 2018; 13 20 21 |
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| Title | Investigations on Brain Tumor Classification Using Hybrid Machine Learning Algorithms |
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