… convolutional neural network (DCNN) is presented in this paper. A patch … In this study,
multiple deep learning based architectures for brain tumor segmentation are presented. A patch …

… In this study, we explore better ways to segment the LGG based on the CNN in the binary
framework. We firstly studied the effect of different depths and the number of neurons in the …

… gliomas into LGG and HGG by using a deep CNN scheme. Two methods have been developed
for glioma … : (a) 3D brain tumor segmentation based on a modification of the popular U-…

… The segmentation model used in this work was based on CNN with the U-net architecture
[25]. As shown in Fig. 2A, the U-net consists of 4 blocks of layers which contain 2 convolution …

… In this work, we have described an automated method for segmenting and classifying the
brain tumor including glioma, meningioma, and pituitary in MRI images. The following are the …

… of gliomas. In this study, we propose a Convolutional Neural Network (CNN)-based framework
for non-… The proposed framework incorporates two novel CNN architectures. The first …

… deep learning-based framework for automated segmentation and prediction of biomarkers
and prognosis in patients with gliomas… they (1) had a diagnosis of glioma with known surgical …

… segment the glioma cells in Magnetic Resonance Imaging (MRI) using faster Region based
Convolutional Neural Network (R-CNN) … the region of interest that is glioma cells, with higher …

… This paper proposes a deep convolutional neural network … The proposed brain tumor
segmentation model is mainly … studies the segmentation method of glioma based on the deep …

… custom convolutional neural network (CNN) based deep learning … glioma grade prediction.
We trained and tested the models based on pathology-proven 104 clinical cases with glioma (…