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Original scientific paper

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

Bidirectional ConvLSTMXNet for Brain Tumor Segmentation of MR Images

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


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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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