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Your search keyword '"Three-class tumor classification"' showing total 2 results
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2 results on '"Three-class tumor classification"'

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

Author

Kokkalla, Srinath, Kakarla, Jagadeesh, Venkateswarlu, Isunuri B., and Singh, Munesh

Subjects
*TUMOR classification, *BRAIN tumors, *KEY performance indicators (Management), *BRAIN imaging, *RETINAL blood vessels
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. [ABSTRACT FROM AUTHOR]

Published
2021
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2. Three-class brain tumor classification using deep dense inception residual network

Author

Isunuri Bala Venkateswarlu, Munesh Singh, Jagadeesh Kakarla, and Srinath Kokkalla

Subjects
0209 industrial biotechnology, Computer science, Brain tumor, Computational intelligence, 02 engineering and technology, Residual, Theoretical Computer Science, Image (mathematics), 020901 industrial engineering & automation, 0202 electrical engineering, electronic engineering, information engineering, medicine, Contextual image classification, business.industry, Data Analytics and Machine Learning, Pattern recognition, medicine.disease, Class (biology), Inception residual network, ComputingMethodologies_PATTERNRECOGNITION, Deep dense network, Three-class tumor classification, Softmax function, Key (cryptography), 020201 artificial intelligence & image processing, Geometry and Topology, Artificial intelligence, business, Software, Brain tumor classification
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.

Published
2021

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