Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

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 …

Optical remote sensing images (RSIs) have been widely used in many applications, and one
of the interesting issues about optical RSIs is the salient object detection (SOD). However …

For early detection of cancer tumors, the semantic segmentation based technique is
proposed because the existing numerous methods fail while classifying due to accuracy and …

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 …

Glioma is the most prevalent and dangerous type of brain tumor which can be life-
threatening when its grade is high. The early detection of these tumors can improve and …

Several techniques are used to detect brain tumors in the medical research field; however,
Magnetic Resonance Imaging (MRI) is still the most effective technique used by experts …

Brain tumor segmentation from MRIs has always been a challenging task for radiologists,
therefore, an automatic and generalized system to address this task is needed. Among all …

One of the primary challenges in applying deep learning approaches to medical imaging is
the limited availability of data due to various factors. These factors include concerns about …

Recent years have witnessed a growing interest in the use of U-Net and its improvement. It
is one of the classic semantic segmentation networks with an encoder-decoder architecture …