TY - GEN

T1 - Improving Brain Tumor Multiclass Classification With Semantic Features

AU - Sao Khue, Luu Minh

AU - Pavlovskiy, Evgeniy

N1 - Funding Information: ACKNOWLEDGMENT The reported study was funded by RFBR according to the research project No 19-29-01103. Publisher Copyright: © 2022 IEEE.

PY - 2022

Y1 - 2022

N2 - Histopathological examination of biopsy tissues is still utilized to diagnose and classify brain cancers today. The current approach is inconvenient, time-consuming, and prone to human mistake. These disadvantages emphasize the significance of establishing a fully automated deep learning-based system for classifying brain tumors. In this paper, we suggest an approach to improve the classification for four types of brain tumors by providing the classifier with segmentation as semantic features. 1,452 multi model magnetic resonance images from the Siberian Brain Tumor Dataset (SBT) are used for training, validation, and testing. The training and validation are implemented with our experimental simple convolutional neural network and a pre-trained VGG16. Best performed models are selected and tested on both SBT and the Brain Tumor Segmentation Challenge 2020 dataset (BraTS). The models with segmentation outperform all models without segmentation on the same dataset. We also found that, compare to a general purposed network such as VGG16, a simple convolutional neural network trained on a specific task have better generalization when tested with a public dataset.

AB - Histopathological examination of biopsy tissues is still utilized to diagnose and classify brain cancers today. The current approach is inconvenient, time-consuming, and prone to human mistake. These disadvantages emphasize the significance of establishing a fully automated deep learning-based system for classifying brain tumors. In this paper, we suggest an approach to improve the classification for four types of brain tumors by providing the classifier with segmentation as semantic features. 1,452 multi model magnetic resonance images from the Siberian Brain Tumor Dataset (SBT) are used for training, validation, and testing. The training and validation are implemented with our experimental simple convolutional neural network and a pre-trained VGG16. Best performed models are selected and tested on both SBT and the Brain Tumor Segmentation Challenge 2020 dataset (BraTS). The models with segmentation outperform all models without segmentation on the same dataset. We also found that, compare to a general purposed network such as VGG16, a simple convolutional neural network trained on a specific task have better generalization when tested with a public dataset.

KW - biomedical images processing

KW - brain tumor classification

KW - convolutional neural networks

KW - deep learning

UR - http://www.scopus.com/inward/record.url?scp=85138464938&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/b3ed3928-1851-3e82-8add-6a389ae72333/

U2 - 10.1109/CSGB56354.2022.9865366

DO - 10.1109/CSGB56354.2022.9865366

M3 - Conference contribution

AN - SCOPUS:85138464938

T3 - Proceedings - 2022 Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2022

SP - 150

EP - 154

BT - Proceedings - 2022 Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2022

Y2 - 7 July 2022 through 8 July 2022

ER -