Recently, deep learning, especially convolutional neural networks, has achieved the
remarkable results in natural image classification and segmentation. At the same time, in the …

Brain tumor is considered as one of the deadliest and most common form of cancer both in
children and in adults. Consequently, determining the correct type of brain tumor in early …

Objectives Medical image analysis practices face challenges that can potentially be
addressed with algorithm-based segmentation tools. In this study, we map the field of …

Brain tumor (BTs) is considered one of the deadly, destructive, and belligerent disease, that
shortens the average life span of patients. Patients with misdiagnosed and insufficient …

Most medical images suffer from inadequate contrast and brightness, which leads to blurred
or weak edges (low contrast) between adjacent tissues resulting in poor segmentation and …

The brain is a significant organ that controls all activities of the body parts. A Brain Tumor
(BT) is a group of tissues, which are structured by the gradual accumulation of irregular cells …

Brain tumor segmentation is one of the main challenging problems in computer vision and
its early diagnosis is critical to clinics. Segmentation needs to be accurate, efficient and …

Abstract Magnetic Resonance Imaging (MRI) is a medical imaging modality that is
commonly employed for the analysis of different diseases. However, these images come …

Since the brain is the human body's primary command and control center, brain cancer is
one of the most dangerous cancers. Automatic segmentation of brain tumors from multi …

In computational and clinical environments, autoclassification of brain magnetic resonance
image (MRI) slices as normal and abnormal is challenging. The purpose of this study is to …