Background Brain cancer is a destructive and life-threatening disease that imposes
immense negative effects on patients' lives. Therefore, the detection of brain tumors at an …

In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …

Background and Objectives: Clinical diagnosis has become very significant in today's health
system. The most serious disease and the leading cause of mortality globally is brain cancer …

Abstract 3D MRI Brain Tumor Segmentation is of great significance in clinical diagnosis and
treatment. Accurate segmentation results are critical for localization and spatial distribution …

With the advancement in technology, machine learning can be applied to diagnose the
mass/tumor in the brain using magnetic resonance imaging (MRI). This work proposes a …

The automatic segmentation of brain tumors in Magnetic Resonance Imaging (MRI) plays a
major role in accurate diagnosis and treatment planning. The present study proposes a new …

The recent development in the areas of deep learning and deep convolutional neural
networks has significantly progressed and advanced the field of computer vision (CV) and …

Brain tumors have become a severe medical complication in recent years due to their high
fatality rate. Radiologists segment the tumor manually, which is time-consuming, error …

With the advent of advancements in deep learning approaches, such as deep convolution
neural network, residual neural network, adversarial network; U-Net architectures are most …

Brain tumor segmentation (BTS) in magnetic resonance image (MRI) is crucial for brain
tumor diagnosis, cancer management and research purposes. With the great success of the …