FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4229014397> ?p ?o ?g. }

• W4229014397 endingPage "12" @default.
• W4229014397 startingPage "1" @default.
• W4229014397 abstract "Today, many people under the age of 10 are being examined for brain-related issues, including tumours, without displaying any symptoms. It is not unusual for children to develop brain-related concerns such as tumours and central nervous system disorders, which may affect 15% of the population. Medical experts believe that the irregular eating habits (junk food) and the consumption of pesticide-tainted fruits and vegetables are to blame. The human body is naturally resistant to harmful gears, but only up to a point. If it exceeds the limit, a cell manipulation process is automatically initiated that can remove dangerous inactive tissues from the cell membrane and later grows into tumour blockage in the human body. Thus, the adoption of an advanced computer-based diagnostic system is highly recommended in order to generate visually enhanced images for anomaly identification and infectious tissue segmentation. In most cases, an MR image is chosen since it is easier to distinguish between affected and nonaffected tissue. Conventional convolution neural network (CCNN) mapping and feature extraction are difficult because of the vast volume of data. In addition, it takes a lengthy time for the MRI scanning process to obtain diverse positions for anomaly identification. Aside from the discomfort, the patient may experience motion abnormalities. Recurrent neural network (RNN) classifies tumour regions into several isolated portions much faster and more accurately, so that it can be prevented. To remove motion artefacts from dynamic multicontrast MR images, a novel long short-term memory- (LSTM-) based RNN framework is introduced in this research. With this method, the MR image’s visual quality is improved over CCNN while simultaneously mapping a larger volume and extracting more quiet characteristics than CCNN can. DC-CNN, SMSR-CNN, FMSI-CNN, and DRCA-CNN results are compared. For both low and high signal-to-noise ratios, the suggested LSTM-based RNN framework has gained reasonable feature intelligibility (SNRs). In comparison to previous approaches, it requires less computing and has higher accuracy when it comes to detecting infected portions." @default.
• W4229014397 created "2022-05-08" @default.
• W4229014397 creator A5012659344 @default.
• W4229014397 creator A5067888843 @default.
• W4229014397 creator A5073975267 @default.
• W4229014397 creator A5076391523 @default.
• W4229014397 creator A5079873660 @default.
• W4229014397 creator A5087092240 @default.
• W4229014397 date "2022-05-04" @default.
• W4229014397 modified "2023-10-17" @default.
• W4229014397 title "LSTM-Based RNN Framework to Remove Motion Artifacts in Dynamic Multicontrast MR Images with Registration Model" @default.
• W4229014397 cites W1544547617 @default.
• W4229014397 cites W1603653760 @default.
• W4229014397 cites W1906516534 @default.
• W4229014397 cites W1966707541 @default.
• W4229014397 cites W1992284218 @default.
• W4229014397 cites W2015216534 @default.
• W4229014397 cites W2035186208 @default.
• W4229014397 cites W2044029912 @default.
• W4229014397 cites W2066213004 @default.
• W4229014397 cites W2070813603 @default.
• W4229014397 cites W2085610308 @default.
• W4229014397 cites W2088553141 @default.
• W4229014397 cites W2115242586 @default.
• W4229014397 cites W2117271072 @default.
• W4229014397 cites W2126446504 @default.
• W4229014397 cites W2136177244 @default.
• W4229014397 cites W2161663128 @default.
• W4229014397 cites W2170493694 @default.
• W4229014397 cites W2205795857 @default.
• W4229014397 cites W2206424798 @default.
• W4229014397 cites W2227471160 @default.
• W4229014397 cites W2331128040 @default.
• W4229014397 cites W2594014149 @default.
• W4229014397 cites W2753693376 @default.
• W4229014397 cites W2804047627 @default.
• W4229014397 cites W2805553949 @default.
• W4229014397 cites W2964070360 @default.
• W4229014397 cites W3017147892 @default.
• W4229014397 cites W3095681026 @default.
• W4229014397 cites W3105445034 @default.
• W4229014397 cites W3152645799 @default.
• W4229014397 cites W3155516693 @default.
• W4229014397 cites W3206101337 @default.
• W4229014397 cites W4200410702 @default.
• W4229014397 cites W4206735239 @default.
• W4229014397 doi "https://doi.org/10.1155/2022/5906877" @default.
• W4229014397 hasPublicationYear "2022" @default.
• W4229014397 type Work @default.
• W4229014397 citedByCount "5" @default.
• W4229014397 countsByYear W42290143972022 @default.
• W4229014397 countsByYear W42290143972023 @default.
• W4229014397 crossrefType "journal-article" @default.
• W4229014397 hasAuthorship W4229014397A5012659344 @default.
• W4229014397 hasAuthorship W4229014397A5067888843 @default.
• W4229014397 hasAuthorship W4229014397A5073975267 @default.
• W4229014397 hasAuthorship W4229014397A5076391523 @default.
• W4229014397 hasAuthorship W4229014397A5079873660 @default.
• W4229014397 hasAuthorship W4229014397A5087092240 @default.
• W4229014397 hasBestOaLocation W42290143971 @default.
• W4229014397 hasConcept C108583219 @default.
• W4229014397 hasConcept C111919701 @default.
• W4229014397 hasConcept C116834253 @default.
• W4229014397 hasConcept C147168706 @default.
• W4229014397 hasConcept C153180895 @default.
• W4229014397 hasConcept C154945302 @default.
• W4229014397 hasConcept C2908647359 @default.
• W4229014397 hasConcept C31972630 @default.
• W4229014397 hasConcept C41008148 @default.
• W4229014397 hasConcept C50644808 @default.
• W4229014397 hasConcept C59822182 @default.
• W4229014397 hasConcept C71924100 @default.
• W4229014397 hasConcept C739882 @default.
• W4229014397 hasConcept C81363708 @default.
• W4229014397 hasConcept C86803240 @default.
• W4229014397 hasConcept C89600930 @default.
• W4229014397 hasConcept C98045186 @default.
• W4229014397 hasConcept C99454951 @default.
• W4229014397 hasConceptScore W4229014397C108583219 @default.
• W4229014397 hasConceptScore W4229014397C111919701 @default.
• W4229014397 hasConceptScore W4229014397C116834253 @default.
• W4229014397 hasConceptScore W4229014397C147168706 @default.
• W4229014397 hasConceptScore W4229014397C153180895 @default.
• W4229014397 hasConceptScore W4229014397C154945302 @default.
• W4229014397 hasConceptScore W4229014397C2908647359 @default.
• W4229014397 hasConceptScore W4229014397C31972630 @default.
• W4229014397 hasConceptScore W4229014397C41008148 @default.
• W4229014397 hasConceptScore W4229014397C50644808 @default.
• W4229014397 hasConceptScore W4229014397C59822182 @default.
• W4229014397 hasConceptScore W4229014397C71924100 @default.
• W4229014397 hasConceptScore W4229014397C739882 @default.
• W4229014397 hasConceptScore W4229014397C81363708 @default.
• W4229014397 hasConceptScore W4229014397C86803240 @default.
• W4229014397 hasConceptScore W4229014397C89600930 @default.
• W4229014397 hasConceptScore W4229014397C98045186 @default.
• W4229014397 hasConceptScore W4229014397C99454951 @default.
• W4229014397 hasLocation W42290143971 @default.
• W4229014397 hasOpenAccess W4229014397 @default.

• next