The use of artificial intelligence (AI) in medicine is beginning to alter current procedures in
prevention, diagnosis, treatment, amelioration, cure of disease and other physical and …

Semantic-based segmentation (Semseg) methods play an essential part in medical imaging
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …

Background Detecting brain tumors in their early stages is crucial. Brain tumors are
classified by biopsy, which can only be performed through definitive brain surgery …

Semantic segmentation has a broad range of applications in a variety of domains including
land coverage analysis, autonomous driving, and medical image analysis. Convolutional …

Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

Brain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial
phase, it may lead to death. Despite many significant efforts and promising outcomes in this …

Brain MR images are the most suitable method for detecting chronic nerve diseases such as
brain tumors, strokes, dementia, and multiple sclerosis. They are also used as the most …

In this paper, we present a fully automatic brain tumor segmentation and classification model
using a Deep Convolutional Neural Network that includes a multiscale approach. One of the …

Blood clots in the brain are frequently caused by brain tumors. Early detection of these clots
has the potential to significantly lower morbidity and mortality in cases of brain cancer. It is …

We apply nnU-Net to the segmentation task of the BraTS 2020 challenge. The unmodified
nnU-Net baseline configuration already achieves a respectable result. By incorporating …