Multi-objective Archimedes Optimization Algorithm with Fusion-based Deep Learning model for brain tumor diagnosis and classification

Earlier identification of brain tumors (BT) is essential to increase the survival rate. Magnetic Resonance Imaging (MRI) is a commonly employed method that records brain abnormalities by the use of several modalities for clinical study. The recently developed computer vision and image processing sch...

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Veröffentlicht in:	Multimedia tools and applications Jg. 82; H. 11; S. 16985 - 17007
Hauptverfasser:	Devanathan, B., Kamarasan, M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.05.2023 Springer Nature B.V
Schlagworte:	Abnormalities Algorithms Brain Brain cancer Classification Computer Communication Networks Computer Science Computer vision Data Structures and Information Theory Deep learning Diagnosis Entropy (Information theory) Feature extraction Image contrast Image enhancement Image processing Image segmentation Machine learning Magnetic resonance imaging Medical imaging Multimedia Information Systems Multiple objective analysis Optimization Optimization algorithms Special Purpose and Application-Based Systems Tumors Brain tumor Deep learning Magnetic resonance imaging Multiobjective optimization Fusion models Computer aided diagnosis Image classification
ISSN:	1380-7501, 1573-7721
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Abstract	Earlier identification of brain tumors (BT) is essential to increase the survival rate. Magnetic Resonance Imaging (MRI) is a commonly employed method that records brain abnormalities by the use of several modalities for clinical study. The recently developed computer vision and image processing schemes can be used for the detection and localization of tumor regions in the brain, which can be utilized for further treatment. In this regard, the study presents a novel Multiobjective Archimedes Optimization Algorithm with Fusion based Deep Learning (MOAOA-FDL) technique for brain tumor diagnosis and classification. In addition, the MOAOA-FDL technique preprocesses the MRI images via contrast enhancement and skull stripping. Moreover, AOA with Shannon entropy based multi-level thresholding approach is developed for medical image segmentation. Furthermore, the fusion of two deep learning models namely MobileNet and EfficientNet models is employed for feature extraction process. Finally, the AOA with long short term memory (LSTM) method is applied for classification model and thus allocates proper class label to it. The AOA is used to properly choose the hyper parameters like batch size, learning rate, and epoch count. The design of fusion process and MOAOA for BT diagnosis demonstrates the innovation of this study. For showcasing the better performance of the MOAOA-FDL method, a series of simulations have been executed utilizing benchmark dataset. The experimental outcome shows that the MOAOA-FDL method has outperformed the other recent approaches in terms of several performance measures.
AbstractList	Earlier identification of brain tumors (BT) is essential to increase the survival rate. Magnetic Resonance Imaging (MRI) is a commonly employed method that records brain abnormalities by the use of several modalities for clinical study. The recently developed computer vision and image processing schemes can be used for the detection and localization of tumor regions in the brain, which can be utilized for further treatment. In this regard, the study presents a novel Multiobjective Archimedes Optimization Algorithm with Fusion based Deep Learning (MOAOA-FDL) technique for brain tumor diagnosis and classification. In addition, the MOAOA-FDL technique preprocesses the MRI images via contrast enhancement and skull stripping. Moreover, AOA with Shannon entropy based multi-level thresholding approach is developed for medical image segmentation. Furthermore, the fusion of two deep learning models namely MobileNet and EfficientNet models is employed for feature extraction process. Finally, the AOA with long short term memory (LSTM) method is applied for classification model and thus allocates proper class label to it. The AOA is used to properly choose the hyper parameters like batch size, learning rate, and epoch count. The design of fusion process and MOAOA for BT diagnosis demonstrates the innovation of this study. For showcasing the better performance of the MOAOA-FDL method, a series of simulations have been executed utilizing benchmark dataset. The experimental outcome shows that the MOAOA-FDL method has outperformed the other recent approaches in terms of several performance measures.
Author	Devanathan, B. Kamarasan, M.
Author_xml	– sequence: 1 givenname: B. surname: Devanathan fullname: Devanathan, B. email: devacisau@gmail.com organization: Department of Computer and Information Science, Annamalai University – sequence: 2 givenname: M. surname: Kamarasan fullname: Kamarasan, M. organization: Department of Computer and Information Science, Annamalai University
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Cites_doi	10.3390/s20102809 10.3390/s21165571 10.1007/s00521-020-05332-5 10.1016/j.patrec.2017.10.036 10.1016/j.cma.2020.113609 10.3390/s21082852 10.1007/s42452-019-1682-y 10.1016/j.patrec.2017.05.028 10.3390/diagnostics11112017 10.1002/jemt.23688 10.1016/j.future.2018.04.065 10.1007/s12652-021-03612-z 10.1007/s40747-021-00321-0 10.1016/j.measurement.2019.07.058 10.1109/ACCESS.2020.3016319 10.1002/jemt.23694 10.1109/ICSSE50014.2020.9219319 10.1007/s00521-021-05841-x 10.3390/healthcare9020153 10.1016/B978-0-12-819061-6.00004-5
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SubjectTerms	Abnormalities Algorithms Brain Brain cancer Classification Computer Communication Networks Computer Science Computer vision Data Structures and Information Theory Deep learning Diagnosis Entropy (Information theory) Feature extraction Image contrast Image enhancement Image processing Image segmentation Machine learning Magnetic resonance imaging Medical imaging Multimedia Information Systems Multiple objective analysis Optimization Optimization algorithms Special Purpose and Application-Based Systems Tumors
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Title	Multi-objective Archimedes Optimization Algorithm with Fusion-based Deep Learning model for brain tumor diagnosis and classification
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