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Three-class brain tumor classification using deep dense inception residual network.

Authors
  • Kokkalla, Srinath1
  • Kakarla, Jagadeesh1
  • Venkateswarlu, Isunuri B1
  • Singh, Munesh1
  • 1 IIITDM Kancheepuram, Chennai, India. , (India)
Type
Published Article
Journal
Soft computing
Publication Date
Apr 16, 2021
Pages
1–9
Identifiers
DOI: 10.1007/s00500-021-05748-8
PMID: 33897297
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Three-class brain tumor classification becomes a contemporary research task due to the distinct characteristics of tumors. The existing proposals employ deep neural networks for the three-class classification. However, achieving high accuracy is still an endless challenge in brain image classification. We have proposed a deep dense inception residual network for three-class brain tumor classification. We have customized the output layer of Inception ResNet v2 with a deep dense network and a softmax layer. The deep dense network has improved the classification accuracy of the proposed model. The proposed model has been evaluated using key performance metrics on a publicly available brain tumor image dataset having 3064 images. Our proposed model outperforms the existing model with a mean accuracy of 99.69%. Further, similar performance has been obtained on noisy data. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.

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