Glioma is one of the most common and aggressive brain tumors. Segmentation and
subsequent quantitative analysis of brain tumor MRI are routine and crucial for treatment …

Deep learning techniques have been widely adopted for learning task-adaptive features in
image segmentation applications, such as brain tumor segmentation. However, most of …

Purpose Gliomas are the most common and aggressive type of brain tumors due to their
infiltrative nature and rapid progression. The process of distinguishing tumor boundaries …

Automatic segmentation of intracranial brain tumors in three-dimensional (3D) image series
is critical in screening and diagnosing related diseases. However, there are various …

Detecting brain tumors plays an important role in patients' lives as it can help specialists
save them or let them succumb to a terminal illness otherwise. Magnetic Resonance …

In this study, brain tumor substructures are segmented using 2D fully convolutional neural
networks. A number of modifications such as double convolution layers, inception modules …

Background Glioma is the most common brain malignant tumor, with a high morbidity rate
and a mortality rate of more than three percent, which seriously endangers human health …

Recently deep learning has been playing a major role in the field of computer vision. One of
its applications is the reduction of human judgment in the diagnosis of diseases. Especially …

Researchers have given immense consideration to unsupervised approaches because of
their tendency for automatic feature generation and excellent performance with a reduced …

Purpose: Segmentation is one of the critical steps in analyzing medical images since it
provides meaningful information for the diagnosis, monitoring, and treatment of brain tumors …