TumorAwareNet: Deep representation learning with attention based sparse convolutional denoising autoencoder for brain tumor recognition

Learning discriminate representations from images plays crucial role in medical image analysis. The attention mechanism, on the other hand, leads to breakthrough results in the computer vision field by allowing models to provide varying levels of focus across image regions. In this work, we present...

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Veröffentlicht in:	Multimedia tools and applications Jg. 83; H. 8; S. 22099 - 22117
Hauptverfasser:	Bodapati, Jyostna Devi, Balaji, Bharadwaj Bagepalli
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.03.2024 Springer Nature B.V
Schlagworte:	Artificial neural networks Brain cancer Brain research Computer Communication Networks Computer Science Computer vision Data Structures and Information Theory Datasets Image analysis Machine learning Magnetic resonance imaging Medical imaging Multimedia Multimedia Information Systems Neural networks Noise reduction Representations Special Purpose and Application-Based Systems Success Support vector machines Tumors Reconstruction convolutional auto-encoder Pre-trained Convolutional Neural Networks (CNNs) Spatial attention Convolutional support vector classifier Brain tumor recognition Deep features Sparse Convolutional Denoising Autoencoder (SCDA)
ISSN:	1573-7721, 1380-7501, 1573-7721
Online-Zugang:	Volltext
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