Original scientific paper

https://doi.org/10.31803/tg-20210204162414

Bidirectional ConvLSTMXNet for Brain Tumor Segmentation of MR Images

M. Ravikumar orcid.org/0000-0001-5450-7788 ; Computer Science Dept., Kuvempu University, Shankaraghatta 577451, Shimoga Dist, Karnataka, India
B. J. Shivaprasad orcid.org/0000-0001-9863-1094 ; Computer Science Dept., Kuvempu University, Shankaraghatta 577451, Shimoga Dist, Karnataka, India

Abstract

In recent years, deep learning based networks have achieved good performance in brain tumour segmentation of MR Image. Among the existing networks, U-Net has been successfully applied. In this paper, it is propose deep-learning based Bidirectional Convolutional LSTM XNet (BConvLSTMXNet) for segmentation of brain tumor and using GoogLeNet classify tumor & non-tumor. Evaluated on BRATS-2019 data-set and the results are obtained for classification of tumor and non-tumor with Accuracy: 0.91, Precision: 0.95, Recall: 1.00 & F1-Score: 0.92. Similarly for segmentation of brain tumor obtained Accuracy: 0.99, Specificity: 0.98, Sensitivity: 0.91, Precision: 0.91 & F1-Score: 0.88.

Keywords

ConvLSTM; GoogLeNet; Linear Transformation (LT); Notch Filter; X-Net

Hrčak ID:

253019

URI

https://hrcak.srce.hr/253019

Publication date:

3.3.2021.

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