Brain Tumor Detection by Using Stacked Autoencoders in Deep Learning.
Brain tumor detection depicts a tough job because of its shape, size and appearance variations. In this manuscript, a deep learning model is deployed to predict input slices as a tumor (unhealthy)/non-tumor (healthy). This manuscript employs a high pass filter image to prominent the inhomogeneities...
| Publicado en: | Journal of Medical Systems Vol. 44; no. 2; pp. 1 - 13 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images equations & formulas tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2020
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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=141512186&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141512186 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Feb2020 vid: 44 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 141512186 141512186 141512186 10.1007/s10916-019-1483-2 141512186 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Brain Tumor Detection by Using Stacked Autoencoders in Deep Learning. aug: au: Amin, Javaria Sharif, Muhammad Gul, Nadia Raza, Mudassar Anjum, Muhammad Almas Nisar, Muhammad Wasif Bukhari, Syed Ahmad Chan affil: Department of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad, Pakistan sug: subj: Brain Neoplasms Diagnosis Deep Learning Neural Networks (Computer) Magnetic Resonance Imaging Methods Autoencoder Utilization Image Processing, Computer Assisted Algorithms Data Analysis Software Glioma Diagnosis ab: Brain tumor detection depicts a tough job because of its shape, size and appearance variations. In this manuscript, a deep learning model is deployed to predict input slices as a tumor (unhealthy)/non-tumor (healthy). This manuscript employs a high pass filter image to prominent the inhomogeneities field effect of the MR slices and fused with the input slices. Moreover, the median filter is applied to the fused slices. The resultant slices quality is improved with smoothen and highlighted edges of the input slices. After that, based on these slices' intensity, a 4-connected seed growing algorithm is applied, where optimal threshold clusters the similar pixels from the input slices. The segmented slices are then supplied to the fine-tuned two layers proposed stacked sparse autoencoder (SSAE) model. The hyperparameters of the model are selected after extensive experiments. At the first layer, 200 hidden units and at the second layer 400 hidden units are utilized. The testing is performed on the softmax layer for the prediction of the images having tumors and no tumors. The suggested model is trained and checked on BRATS datasets i.e., 2012(challenge and synthetic), 2013, and 2013 Leaderboard, 2014, and 2015 datasets. The presented model is evaluated with a number of performance metrics which demonstrates the improved performance. 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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