Metaheuristic Optimization-Driven Novel Deep Learning Approach for Brain Tumor Segmentation.
Brain tumor has the foremost distinguished etiology of high morality. Neoplasm, a categorization of brain tumors, is very operative in distinguishing and determining the tumor's exact location in the brain. Magnetic resonance imaging (MRI) is an efficient noninvasive technique for the anatomical exa...
| Published in: | BioMed Research International pp. 1 - 16 |
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| Main Authors: | , , |
| Format: | diagnostic images equations & formulas tables/charts Journal Article |
| Published: |
Wiley-Blackwell
8/18/2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158604386&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158604386 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/18/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 158604386 158604386 158604386 10.1155/2022/2980691 158604386 ppf: 1 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Metaheuristic Optimization-Driven Novel Deep Learning Approach for Brain Tumor Segmentation. aug: au: Kalpana, R. Bennet, M. Anto Rahmani, Abdul Wahab affil: Department of Electronics and Communication Engineering, VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu 600062, India sug: subj: Brain Neoplasms Diagnosis Magnetic Resonance Imaging Utilization Deep Learning Methods Human Brain Neoplasms Classification Diagnosis, Brain Algorithms Neural Networks (Computer) ab: Brain tumor has the foremost distinguished etiology of high morality. Neoplasm, a categorization of brain tumors, is very operative in distinguishing and determining the tumor's exact location in the brain. Magnetic resonance imaging (MRI) is an efficient noninvasive technique for the anatomical examination of brain tumors. Growth tissues have a distinguishable look in MRI pictures in order that they are unit-wide used for brain tumor feature extraction. The existing research algorithms for brain tumors have some limitations such as different qualities, low sensitivity, and diagnosing the tumor at its stages. In this particular piece of research, an innovative method of optimization known as the procedure for lightning attachment algorithm (PLA) is used, and for the purpose of classification, a CNN model known as DenseNet-169 is applied. PLA was used in order to optimize the growth, and a network model known as the DenseNet-169 model was utilized in order to extract the various growth-optimization choices. First, the MR images of the brain were preprocessed to remove any outliers. Next, the Dense Net-169 CNN model was used to extract network choices from the MR images. In addition, it is used to execute the function of a classifier in order to identify the growth as either an aberrant growth or a traditional growth. In addition, the publicly benchmarked datasets that are widely utilized have validated the algorithmic rule that was granted. The planned system demonstrates the satisfactory accuracy in getting ready to on the dataset and outperforms many of the notable current techniques. pubtype: Academic Journal doctype: diagnostic images equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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