Open AccessBidirectional ConvLSTMXNet for Brain Tumor Segmentation of MR Images
Author(s) -
M. Ravikumar,
B. J. Shivaprasad
Publication year - 2021
Publication title -
tehnički glasnik
Language(s) - English
Resource type - Journals
eISSN - 1848-5588
pISSN - 1846-6168
DOI - 10.31803/tg-20210204162414
Subject(s) - artificial intelligence , segmentation , f1 score , brain tumor , deep learning , pattern recognition (psychology) , computer science , convolutional neural network , precision and recall , medicine , pathology
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.