Segmentation of brain tumors is of great importance for patients in clinical diagnosis and
treatment. For this reason, experts try to identify border regions of special importance using …

Aiming at the task of automatic brain tumor segmentation, this paper proposes a new
DenseTrans network. In order to alleviate the problem that convolutional neural networks …

Radiologists encounter significant challenges when segmenting and determining brain
tumors in patients because this information assists in treatment planning. The utilization of …

Accurately identifying tumors from MRI scans is of the utmost importance for clinical
diagnostics and when making plans regarding brain tumor treatment. However, manual …

Gliomas are the most common and aggressive form of all brain tumors, with a median
survival rate of fewer than two years, especially for the highest-grade glioma patient …

Introduction Recently, the Transformer model and its variants have been a great success in
terms of computer vision, and have surpassed the performance of convolutional neural …

Accurate segmentation of brain gliomas (BG) is a crucial and challenging task for effective
treatment planning in BG therapy. This study presents the weight loss function and dropout …

Recently, the Deep Neural Networks (DNNs) have had a large impact on imaging process
including medical image segmentation, and the real-valued convolution of DNN has been …

Accurate prediction of survival days (SD) is vital for planning treatments in glioma patients,
as type‐IV tumors typically have a poor prognosis and meager survival rates. SD prediction …

Early-stage 3D brain tumor segmentation from magnetic resonance imaging (MRI) scans is
crucial for prompt and effective treatment. However, this process faces the challenge of …