2019
DOI: 10.11591/ijeecs.v15.i3.pp1313-1321
|View full text |Cite
|
Sign up to set email alerts
|

Automatic segmentation of spinal cord diffusion MR Images for disease location finding

Abstract: <p>Dissemination weighted MR imaging may build the affectability and explicitness of MR imaging for certain pathologic states of the spinal rope yet is once in a while performed as a result of a few specialized issues. We consequently tried a novel stage explored turn reverberation dispersion weighted interleaved reverberation planar imaging arrangement in seven sound volunteers and six patients with intramedullary injuries. We performed dispersion weighted MR imaging of the spinal string with high spati… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
6
0

Year Published

2019
2019
2023
2023

Publication Types

Select...
5
3
1

Relationship

0
9

Authors

Journals

citations
Cited by 15 publications
(6 citation statements)
references
References 24 publications
0
6
0
Order By: Relevance
“…Deep learning is a class of Machine learning algorithms that uses multiple layers to progressively extract higher level features from the raw input. The aim is to make machines like computers think and understand how humans think by imitating the grid of the human brain connection, Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts [19]. Supervised learning algorithm is applied to a dataset that has features and each of those features associated with a label.…”
Section: Deep Learning Models For Intrusion Detection System In Manetmentioning
confidence: 99%
“…Deep learning is a class of Machine learning algorithms that uses multiple layers to progressively extract higher level features from the raw input. The aim is to make machines like computers think and understand how humans think by imitating the grid of the human brain connection, Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts [19]. Supervised learning algorithm is applied to a dataset that has features and each of those features associated with a label.…”
Section: Deep Learning Models For Intrusion Detection System In Manetmentioning
confidence: 99%
“…In this technique all determination datasets are replicated with later hour. The significant function is that is fitness function performed rows and columns have data with a diagonal manner [8]. Before this process characters and datasets, frames have automatically arranged in a association manner.…”
Section: Post Process Dimensionality Reductionmentioning
confidence: 99%
“…Here we use in band amplify and leading (AF) type-II full double relay, this will reduce the intricacy. In band relay allows the same set of density subcarriers for concerted transmission with each user's furniture (UE) [2]. Relay station accept a signal from user furniture, it will first exaggerate the signal then retransmits to the base station (eNB…”
Section: Designed System Figure 1: Block Diagram Overview Of Designed Systemmentioning
confidence: 99%
“…With the feasible rate increase by using SCFDMA in both uplink and down with comparison and power allotment [1]. Throughput expansion by using equal power algorithm and an equal power algorithm with clarification scheme [2]. By seeing the quality of service (QoS) and contrasting power allocation conclusion can increase system throughput [3].…”
Section: Introductionmentioning
confidence: 99%