Matches in SemOpenAlex for { <https://semopenalex.org/work/W2800691917> ?p ?o ?g. }
Showing items 1 to 95 of
95
with 100 items per page.
- W2800691917 abstract "Deep convolutional neural networks (CNNs) have become one of the state-of-the-art methods for image classification in various domains. For biomedical image classification where the number of training images is generally limited, transfer learning using CNNs is often applied. Such technique extracts generic image features from nature image datasets and these features can be directly adopted for feature extraction in smaller datasets. In this paper, we propose a novel deep neural network architecture based on transfer learning for microscopic image classification. In our proposed network, we concatenate the features extracted from three pretrained deep CNNs. The concatenated features are then used to train two fully-connected layers to perform classification. In the experiments on both the 2D-Hela and the PAP-smear datasets, our proposed network architecture produces significant performance gains comparing to the neural network structure that uses only features extracted from single CNN and several traditional classification methods." @default.
- W2800691917 created "2018-05-17" @default.
- W2800691917 creator A5049506273 @default.
- W2800691917 creator A5055510228 @default.
- W2800691917 creator A5060316958 @default.
- W2800691917 creator A5074063024 @default.
- W2800691917 date "2018-01-01" @default.
- W2800691917 modified "2023-10-16" @default.
- W2800691917 title "Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation" @default.
- W2800691917 cites W1533007923 @default.
- W2800691917 cites W1984643873 @default.
- W2800691917 cites W2000266578 @default.
- W2800691917 cites W2062118960 @default.
- W2800691917 cites W2097117768 @default.
- W2800691917 cites W2108598243 @default.
- W2800691917 cites W2112467442 @default.
- W2800691917 cites W2145752049 @default.
- W2800691917 cites W2153944330 @default.
- W2800691917 cites W2162915993 @default.
- W2800691917 cites W2163352848 @default.
- W2800691917 cites W2183341477 @default.
- W2800691917 cites W2194775991 @default.
- W2800691917 cites W2253429366 @default.
- W2800691917 cites W2298071216 @default.
- W2800691917 cites W2526259914 @default.
- W2800691917 cites W2559785631 @default.
- W2800691917 cites W2757621468 @default.
- W2800691917 cites W4248710273 @default.
- W2800691917 doi "https://doi.org/10.1109/iscas.2018.8351550" @default.
- W2800691917 hasPublicationYear "2018" @default.
- W2800691917 type Work @default.
- W2800691917 sameAs 2800691917 @default.
- W2800691917 citedByCount "132" @default.
- W2800691917 countsByYear W28006919172018 @default.
- W2800691917 countsByYear W28006919172019 @default.
- W2800691917 countsByYear W28006919172020 @default.
- W2800691917 countsByYear W28006919172021 @default.
- W2800691917 countsByYear W28006919172022 @default.
- W2800691917 countsByYear W28006919172023 @default.
- W2800691917 crossrefType "proceedings-article" @default.
- W2800691917 hasAuthorship W2800691917A5049506273 @default.
- W2800691917 hasAuthorship W2800691917A5055510228 @default.
- W2800691917 hasAuthorship W2800691917A5060316958 @default.
- W2800691917 hasAuthorship W2800691917A5074063024 @default.
- W2800691917 hasBestOaLocation W28006919172 @default.
- W2800691917 hasConcept C108583219 @default.
- W2800691917 hasConcept C114614502 @default.
- W2800691917 hasConcept C115961682 @default.
- W2800691917 hasConcept C138885662 @default.
- W2800691917 hasConcept C150899416 @default.
- W2800691917 hasConcept C153180895 @default.
- W2800691917 hasConcept C154945302 @default.
- W2800691917 hasConcept C2776401178 @default.
- W2800691917 hasConcept C33923547 @default.
- W2800691917 hasConcept C41008148 @default.
- W2800691917 hasConcept C41895202 @default.
- W2800691917 hasConcept C50644808 @default.
- W2800691917 hasConcept C52622490 @default.
- W2800691917 hasConcept C75294576 @default.
- W2800691917 hasConcept C81363708 @default.
- W2800691917 hasConcept C87619178 @default.
- W2800691917 hasConceptScore W2800691917C108583219 @default.
- W2800691917 hasConceptScore W2800691917C114614502 @default.
- W2800691917 hasConceptScore W2800691917C115961682 @default.
- W2800691917 hasConceptScore W2800691917C138885662 @default.
- W2800691917 hasConceptScore W2800691917C150899416 @default.
- W2800691917 hasConceptScore W2800691917C153180895 @default.
- W2800691917 hasConceptScore W2800691917C154945302 @default.
- W2800691917 hasConceptScore W2800691917C2776401178 @default.
- W2800691917 hasConceptScore W2800691917C33923547 @default.
- W2800691917 hasConceptScore W2800691917C41008148 @default.
- W2800691917 hasConceptScore W2800691917C41895202 @default.
- W2800691917 hasConceptScore W2800691917C50644808 @default.
- W2800691917 hasConceptScore W2800691917C52622490 @default.
- W2800691917 hasConceptScore W2800691917C75294576 @default.
- W2800691917 hasConceptScore W2800691917C81363708 @default.
- W2800691917 hasConceptScore W2800691917C87619178 @default.
- W2800691917 hasLocation W28006919171 @default.
- W2800691917 hasLocation W28006919172 @default.
- W2800691917 hasOpenAccess W2800691917 @default.
- W2800691917 hasPrimaryLocation W28006919171 @default.
- W2800691917 hasRelatedWork W2253429366 @default.
- W2800691917 hasRelatedWork W2952813363 @default.
- W2800691917 hasRelatedWork W3003905048 @default.
- W2800691917 hasRelatedWork W3127975138 @default.
- W2800691917 hasRelatedWork W3135818718 @default.
- W2800691917 hasRelatedWork W3167935049 @default.
- W2800691917 hasRelatedWork W3176438653 @default.
- W2800691917 hasRelatedWork W3183901164 @default.
- W2800691917 hasRelatedWork W4290188444 @default.
- W2800691917 hasRelatedWork W4378678253 @default.
- W2800691917 isParatext "false" @default.
- W2800691917 isRetracted "false" @default.
- W2800691917 magId "2800691917" @default.
- W2800691917 workType "article" @default.