TY - GEN

T1 - Binary Brain Tumor Classification With Semantic Features Using Convolutional Neural Network

AU - Лыу, Минь Шао Кхуэ

AU - Pavlovskiy, Evgeny

N1 - Conference code: 5

PY - 2022/10/25

Y1 - 2022/10/25

N2 - In this study, we provide segmentations of brain tumors as semantic features to a simple convolutional neural network (CNN) to improve the classification results. The Siberian Brain Tumor Dataset (SBT) of 1452 magnetic resonance (MR) images of Russian people’s brains is used for training, validation, and testing. We preprocess MR images by removing the swelling region surrounding a brain tumor (edema) from the segmentation and eliminating slices that contain less than 163 pixels of tumor. The binary classifier is a simple network of four convolutional layers for feature extractions and two linear layers for classification. The network is trained and validated with 5-fold cross-validation. We train another CNN with similar configuration on images without provided segmentation and compare the testing results. The two networks are evaluated with four metrics: accuracy, sensitivity, specificity, and F1 scores. The classifier trained with segmentation achieve the highest accuracy score of 0.92, sensitivity of 0.934, specificity of 0.91, and F1 score of 0.90 using ensemble approach.

AB - In this study, we provide segmentations of brain tumors as semantic features to a simple convolutional neural network (CNN) to improve the classification results. The Siberian Brain Tumor Dataset (SBT) of 1452 magnetic resonance (MR) images of Russian people’s brains is used for training, validation, and testing. We preprocess MR images by removing the swelling region surrounding a brain tumor (edema) from the segmentation and eliminating slices that contain less than 163 pixels of tumor. The binary classifier is a simple network of four convolutional layers for feature extractions and two linear layers for classification. The network is trained and validated with 5-fold cross-validation. We train another CNN with similar configuration on images without provided segmentation and compare the testing results. The two networks are evaluated with four metrics: accuracy, sensitivity, specificity, and F1 scores. The classifier trained with segmentation achieve the highest accuracy score of 0.92, sensitivity of 0.934, specificity of 0.91, and F1 score of 0.90 using ensemble approach.

KW - brain tumor classification

KW - deep learning

KW - biomedical images processing

KW - convolutional neural networks

UR - http://www.scopus.com/inward/record.url?scp=85141856060&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/2974d9b3-aa2d-322b-b7c8-95890fc2a7e9/

U2 - 10.1109/USBEREIT56278.2022.9923403

DO - 10.1109/USBEREIT56278.2022.9923403

M3 - Conference contribution

SN - 9781665460927

T3 - Proceedings - 2022 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2022

SP - 44

EP - 47

BT - Proceedings - 2022 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)

Y2 - 19 September 2022 through 21 September 2022

ER -