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

Magnetic Resonance Imaging (MRI) is a critical imaging technique that provides detailed
visualization of internal structures without harmful radiation. This review focuses on key MRI …

Objective Radiomic and deep learning studies based on magnetic resonance imaging (MRI)
of liver tumor are gradually increasing. Manual segmentation of normal hepatic tissue and …

Cross-modality magnetic resonance (MR) image synthesis can be used to generate missing
modalities from given ones. Existing (supervised learning) methods often require a large …

Precise segmentation of brain tumor from magnetic resonance images (MRIs) is of vital
importance for lesion measurement, surgical planning decision, and clinical treatment …

Computer-aided automatic brain-tumor detection has been a very important biomedical
engineering research problem for years. As the sizes and shapes of tumors vary …

Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal
cancer (CRC) in clinical practice. However, due to scale variation and blurry polyp …

Brain tumor diagnostics rely heavily on Magnetic Resonance Imaging (MRI) for accurate
diagnosis and treatment planning due to its non-invasive nature and detailed soft tissue …

Abstract Magnetic Resonance Imaging (MRI) is a widely used medical imaging technique
that has become an essential tool for diagnosing various diseases and visualizing internal …

By recognizing or regulating human emotions, the affective brain–computer interfaces (BCIs)
could improve human–computer interactions. However, human emotion involves complex …