AdaptAhead Optimization Algorithm for Learning Deep CNN Applied to MRI Segmentation.
Deep learning is one of the subsets of machine learning that is widely used in artificial intelligence (AI) field such as natural language processing and machine vision. The deep convolution neural network (DCNN) extracts high-level concepts from low-level features and it is appropriate for large vo...
| Publicado en: | Journal of Digital Imaging Vol. 32; no. 1; pp. 105 - 116 |
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| Autores principales: | , , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2019
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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=134830549&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134830549 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2019 vid: 32 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 134830549 134830549 134830549 10.1007/s10278-018-0107-6 134830549 ppf: 105 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: AdaptAhead Optimization Algorithm for Learning Deep CNN Applied to MRI Segmentation. aug: au: Hoseini, Farnaz Bayat, Peyman Shahbahrami, Asadollah affil: Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran sug: subj: Algorithms Deep Learning Neural Networks (Computer) Magnetic Resonance Imaging Methods Image Processing, Computer Assisted Methods Brain Neoplasms Diagnosis Human Validity ab: Deep learning is one of the subsets of machine learning that is widely used in artificial intelligence (AI) field such as natural language processing and machine vision. The deep convolution neural network (DCNN) extracts high-level concepts from low-level features and it is appropriate for large volumes of data. In fact, in deep learning, the high-level concepts are defined by low-level features. Previously, in optimization algorithms, the accuracy achieved for network training was less and high-cost function. In this regard, in this study, AdaptAhead optimization algorithm was developed for learning DCNN with robust architecture in relation to the high volume data. The proposed optimization algorithm was validated in multi-modality MR images of BRATS 2015 and BRATS 2016 data sets. Comparison of the proposed optimization algorithm with other commonly used methods represents the improvement of the performance of the proposed optimization algorithm on the relatively large dataset. Using the Dice similarity metric, we report accuracy results on the BRATS 2015 and BRATS 2016 brain tumor segmentation challenge dataset. Results showed that our proposed algorithm is significantly more accurate than other methods as a result of its deep and hierarchical extraction. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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