Brain image segmentation is one of the most time-consuming and challenging procedures in
a clinical environment. Recently, a drastic increase in the number of brain disorders has …

Medical image segmentation is a typical task in medical image processing and critical
foundation in medical image analysis. U-Net is well-liked in medical image segmentation …

Medical imaging is widely used in the diagnosis and treatment of cancer, and artificial
intelligence (AI) has achieved tremendous success in medical image analysis. This paper …

Precise brain tumor segmentation can improve patient prognosis. However, due to the
complicated structure of the human brain, brain tumor segmentation is a challenging task. To …

Medical image segmentation of tissue abnormalities, key organs, or blood vascular system
is of great significance for any computerized diagnostic system. However, automatic …

The fine segmentation of brain tumor, which is instrumental in brain tumor diagnosis,
treatment planning and prognosis, is becoming a research hotspot in medical images …

The automatic and accurate detection and segmentation of brain tumors is a very tedious
and challenging task for medical experts and radiologists. This paper proposes a hybrid …

Automatic segmentation of medical images is a difficult task in the field of computer vision
owing to the various backgrounds, shapes, size, and colors of polyps or tumors. Despite the …

Cancer is one of the most significant causes of death worldwide, accounting for millions of
deaths each year. The fatality rate of cancer is getting higher. Over the last three decades …

Tumor segmentation using magnetic resonance imaging (MRI) plays a significant role in
assisting brain tumor diagnosis and treatment. Recently, U-Net architecture with its variants …