Automatic Brain Tumor Classification via Lion Plus Dragonfly Algorithm.
Denoising, skull stripping, segmentation, feature extraction, and classification are five important processes in this paper's development of a brain tumor classification model. The brain tumor image will be imposed first using the entropy-based trilateral filter to de-noising and this image is impos...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 5; pp. 1382 - 1409 |
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| Autores principales: | , |
| Formato: | diagnostic images equations & formulas review tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159758934&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159758934 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2022 vid: 35 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 159758934 157476697 159758934 159758934 10.1007/s10278-022-00635-6 159758934 ppf: 1382 ppct: 27 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic Brain Tumor Classification via Lion Plus Dragonfly Algorithm. aug: au: Leena, B. Jayanthi, A. N. affil: KGiSL Institute of Technology, Coimbatore, India sug: subj: Brain Neoplasms Classification Brain Neoplasms Pathology Algorithms Diagnostic Imaging Image Processing, Computer Assisted Brain Neoplasms Radiography Skull Anatomy and Histology Neural Networks (Computer) Diffusion of Innovation Prediction Models ab: Denoising, skull stripping, segmentation, feature extraction, and classification are five important processes in this paper's development of a brain tumor classification model. The brain tumor image will be imposed first using the entropy-based trilateral filter to de-noising and this image is imposed to skull stripping by means of morphological partition and Otsu thresholding. Adaptive contrast limited fuzzy adaptive histogram equalization (CLFAHE) is also used in the segmentation process. The gray-level co-occurrence matrix (GLCM) characteristics are derived from the segmented image. The collected GLCM features are used in a hybrid classifier that combines the neural network (NN) and deep belief network (DBN) ideas. As an innovation, the hidden neurons of the two classifiers are modified ideally to improve the prediction model's accuracy. The hidden neurons are optimized using a unique hybrid optimization technique known as lion with dragonfly separation update (L-DSU), which integrates the approaches from both DA and LA. Finally, the suggested model's performance is compared to that of the standard models concerning certain performance measures. pubtype: Academic Journal doctype: diagnostic images equations & formulas review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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