Matches in SemOpenAlex for { <https://semopenalex.org/work/W4306179915> ?p ?o ?g. }
Showing items 1 to 80 of
80
with 100 items per page.
- W4306179915 endingPage "106183" @default.
- W4306179915 startingPage "106183" @default.
- W4306179915 abstract "The brain tumor is one of the deadliest diseases of all cancers. Influenced by the recent developments of convolutional neural networks (CNNs) in medical imaging, we have formed a CNN based model called BMRI-Net for brain tumor classification. As the activation function is one of the important modules of CNN, we have proposed a novel parametric activation function named Parametric Flatten-p Mish (PFpM) to improve the performance. PFpM can tackle the significant disadvantages of the pre-existing activation functions like neuron death and bias shift effect. The parametric approach of PFpM also offers the model some extra flexibility to learn the complex patterns more accurately from the data. To validate our proposed methodology, we have used two brain tumor datasets namely Figshare and Br35H. We have compared the performance of our model with state-of-the-art deep CNN models like DenseNet201, InceptionV3, MobileNetV2, ResNet50 and VGG19. Further, the comparative performance of PFpM has been presented with various activation functions like ReLU, Leaky ReLU, GELU, Swish and Mish. We have performed record-wise and subject-wise (patient-level) experiments for Figshare dataset whereas only record-wise experiments have been performed in case of Br35H dataset due to unavailability of subject-wise information. Further, the model has been validated using hold-out and 5-fold cross-validation techniques. On Figshare dataset, our model has achieved 99.57% overall accuracy with hold-out validation and 98.45% overall accuracy with 5-fold cross validation in case of record-wise data split. On the other hand, the model has achieved 97.91% overall accuracy with hold-out validation and 97.26% overall accuracy with 5-fold cross validation in case of subject-wise data split. Similarly, for Br35H dataset, our model has attained 99% overall accuracy with hold-out validation and 98.33% overall accuracy with 5-fold cross validation using record-wise data split. Hence, our findings can introduce a secondary procedure in the clinical diagnosis of brain tumors. • A unique CNN architecture for brain tumor classification. • A novel parametric piece-wise activation function named PFpM. • Achieved 99.57% and 99% overall classification accuracy on Figshare and Br35H dataset, respectively. • Comparative performance analysis of proposed model with state-of-the-art models. • Comparative performance analysis of models with different activation functions." @default.
- W4306179915 created "2022-10-14" @default.
- W4306179915 creator A5022205148 @default.
- W4306179915 creator A5079597528 @default.
- W4306179915 date "2022-11-01" @default.
- W4306179915 modified "2023-10-05" @default.
- W4306179915 title "A novel Parametric Flatten-p Mish activation function based deep CNN model for brain tumor classification" @default.
- W4306179915 cites W1641498739 @default.
- W4306179915 cites W2005942928 @default.
- W4306179915 cites W2007339694 @default.
- W4306179915 cites W2008620264 @default.
- W4306179915 cites W2112090702 @default.
- W4306179915 cites W2112796928 @default.
- W4306179915 cites W2117539524 @default.
- W4306179915 cites W2124637492 @default.
- W4306179915 cites W2182098131 @default.
- W4306179915 cites W2773746284 @default.
- W4306179915 cites W2884741638 @default.
- W4306179915 cites W2955805844 @default.
- W4306179915 cites W2970416468 @default.
- W4306179915 cites W2972838422 @default.
- W4306179915 cites W2986268970 @default.
- W4306179915 cites W3011430986 @default.
- W4306179915 cites W3024767511 @default.
- W4306179915 cites W3103711278 @default.
- W4306179915 cites W3109059087 @default.
- W4306179915 cites W3134696621 @default.
- W4306179915 cites W3136979370 @default.
- W4306179915 cites W3169684453 @default.
- W4306179915 cites W3208458519 @default.
- W4306179915 cites W3211082193 @default.
- W4306179915 cites W3217453598 @default.
- W4306179915 cites W4200529778 @default.
- W4306179915 cites W4210458173 @default.
- W4306179915 cites W4220934922 @default.
- W4306179915 doi "https://doi.org/10.1016/j.compbiomed.2022.106183" @default.
- W4306179915 hasPublicationYear "2022" @default.
- W4306179915 type Work @default.
- W4306179915 citedByCount "9" @default.
- W4306179915 countsByYear W43061799152023 @default.
- W4306179915 crossrefType "journal-article" @default.
- W4306179915 hasAuthorship W4306179915A5022205148 @default.
- W4306179915 hasAuthorship W4306179915A5079597528 @default.
- W4306179915 hasConcept C105795698 @default.
- W4306179915 hasConcept C117251300 @default.
- W4306179915 hasConcept C14036430 @default.
- W4306179915 hasConcept C153180895 @default.
- W4306179915 hasConcept C154945302 @default.
- W4306179915 hasConcept C33923547 @default.
- W4306179915 hasConcept C41008148 @default.
- W4306179915 hasConcept C78458016 @default.
- W4306179915 hasConcept C86803240 @default.
- W4306179915 hasConceptScore W4306179915C105795698 @default.
- W4306179915 hasConceptScore W4306179915C117251300 @default.
- W4306179915 hasConceptScore W4306179915C14036430 @default.
- W4306179915 hasConceptScore W4306179915C153180895 @default.
- W4306179915 hasConceptScore W4306179915C154945302 @default.
- W4306179915 hasConceptScore W4306179915C33923547 @default.
- W4306179915 hasConceptScore W4306179915C41008148 @default.
- W4306179915 hasConceptScore W4306179915C78458016 @default.
- W4306179915 hasConceptScore W4306179915C86803240 @default.
- W4306179915 hasLocation W43061799151 @default.
- W4306179915 hasOpenAccess W4306179915 @default.
- W4306179915 hasPrimaryLocation W43061799151 @default.
- W4306179915 hasRelatedWork W2033914206 @default.
- W4306179915 hasRelatedWork W2042327336 @default.
- W4306179915 hasRelatedWork W2046077695 @default.
- W4306179915 hasRelatedWork W2146076056 @default.
- W4306179915 hasRelatedWork W2163831990 @default.
- W4306179915 hasRelatedWork W2378160586 @default.
- W4306179915 hasRelatedWork W2996038082 @default.
- W4306179915 hasRelatedWork W3003836766 @default.
- W4306179915 hasRelatedWork W3047965787 @default.
- W4306179915 hasRelatedWork W3184582087 @default.
- W4306179915 hasVolume "150" @default.
- W4306179915 isParatext "false" @default.
- W4306179915 isRetracted "false" @default.
- W4306179915 workType "article" @default.