Matches in SemOpenAlex for { <https://semopenalex.org/work/W3106819668> ?p ?o ?g. }
Showing items 1 to 70 of
70
with 100 items per page.
- W3106819668 endingPage "217847" @default.
- W3106819668 startingPage "217830" @default.
- W3106819668 abstract "Attempt to diagnose Alzheimer’s disease (AD) using imaging modalities is one of the scopes of deep learning. While considering the theoretical background from past studies, we are trying to identify convolutional neural network (CNN) behaviors moving from 2D to 3D architecture. This study aims to explore the output from a variety of CNN models implemented in the MRI or/and PET classification tasks for AD prediction while trying to summarize its characteristics with a variety of parameters that are tuned and changed. There are many architectures available; however, we are testing a basic architecture with a change in the reception area based on the convolutional layer’s kernel size and its strides. The architecture has been categorized as converging, diverging, or equivalent if the filter kernel size is unchanged. This investigation studies a simple encoder based CNN with a sequential flow of features from low-level to high-level feature extraction. The idea is to present a diverging reception area by increasing the filter size and stride from a lower to a higher level. As a result, the feature redundancy is reduced and the trivial features keep on diminishing. The proposed architecture is referred to as ‘divNet’, and several experiments were performed to determine how effective the architecture is in terms of the consumed memory, the number of parameters, running time, classification error, and the generalization error. This study surveys several related experiments by changing the hyper-parameters setting, the architecture selection based on the depth and area of the reception feature, and the data size." @default.
- W3106819668 created "2020-12-07" @default.
- W3106819668 creator A5002773429 @default.
- W3106819668 creator A5049515671 @default.
- W3106819668 date "2020-01-01" @default.
- W3106819668 modified "2023-10-13" @default.
- W3106819668 title "3D CNN Design for the Classification of Alzheimer’s Disease Using Brain MRI and PET" @default.
- W3106819668 cites W1677182931 @default.
- W3106819668 cites W2049586412 @default.
- W3106819668 cites W2062118960 @default.
- W3106819668 cites W2097117768 @default.
- W3106819668 cites W2103212315 @default.
- W3106819668 cites W2146427423 @default.
- W3106819668 cites W2153171432 @default.
- W3106819668 cites W2194775991 @default.
- W3106819668 cites W2291961022 @default.
- W3106819668 cites W2310992461 @default.
- W3106819668 cites W2560235512 @default.
- W3106819668 cites W2592929672 @default.
- W3106819668 cites W2649854718 @default.
- W3106819668 cites W2791282053 @default.
- W3106819668 cites W2805773775 @default.
- W3106819668 cites W2905017682 @default.
- W3106819668 cites W2916257687 @default.
- W3106819668 cites W2921824753 @default.
- W3106819668 cites W2948184028 @default.
- W3106819668 cites W2950651700 @default.
- W3106819668 cites W2963150697 @default.
- W3106819668 cites W3012627974 @default.
- W3106819668 cites W4238233446 @default.
- W3106819668 doi "https://doi.org/10.1109/access.2020.3040486" @default.
- W3106819668 hasPublicationYear "2020" @default.
- W3106819668 type Work @default.
- W3106819668 sameAs 3106819668 @default.
- W3106819668 citedByCount "33" @default.
- W3106819668 countsByYear W31068196682021 @default.
- W3106819668 countsByYear W31068196682022 @default.
- W3106819668 countsByYear W31068196682023 @default.
- W3106819668 crossrefType "journal-article" @default.
- W3106819668 hasAuthorship W3106819668A5002773429 @default.
- W3106819668 hasAuthorship W3106819668A5049515671 @default.
- W3106819668 hasBestOaLocation W31068196681 @default.
- W3106819668 hasConcept C153180895 @default.
- W3106819668 hasConcept C154945302 @default.
- W3106819668 hasConcept C41008148 @default.
- W3106819668 hasConceptScore W3106819668C153180895 @default.
- W3106819668 hasConceptScore W3106819668C154945302 @default.
- W3106819668 hasConceptScore W3106819668C41008148 @default.
- W3106819668 hasFunder F4320322120 @default.
- W3106819668 hasLocation W31068196681 @default.
- W3106819668 hasLocation W31068196682 @default.
- W3106819668 hasOpenAccess W3106819668 @default.
- W3106819668 hasPrimaryLocation W31068196681 @default.
- W3106819668 hasRelatedWork W1978450727 @default.
- W3106819668 hasRelatedWork W2033914206 @default.
- W3106819668 hasRelatedWork W2146076056 @default.
- W3106819668 hasRelatedWork W2147291813 @default.
- W3106819668 hasRelatedWork W2163831990 @default.
- W3106819668 hasRelatedWork W2378160586 @default.
- W3106819668 hasRelatedWork W2380927352 @default.
- W3106819668 hasRelatedWork W3003836766 @default.
- W3106819668 hasRelatedWork W4244943737 @default.
- W3106819668 hasRelatedWork W2289108895 @default.
- W3106819668 hasVolume "8" @default.
- W3106819668 isParatext "false" @default.
- W3106819668 isRetracted "false" @default.
- W3106819668 magId "3106819668" @default.
- W3106819668 workType "article" @default.