Cancer is a condition in which abnormal cells uncontrollably split and damage the body
tissues. Hence, detecting cancer at an early stage is highly essential. Currently, medical …

Among the world's most common cancers, colorectal cancer is the third most severe form of
cancer. Early polyp detection reduces the risk of colorectal cancer, vital for effective …

Among researchers using traditional and new machine learning and deep learning
techniques, 2D medical image segmentation models are popular. Additionally, 3D …

Prevention of colorectal cancer (CRC) by inspecting and removing colorectal polyps has
become a global health priority because CRC is one of the most frequent cancers in the …

Medical image segmentation is crucial for medical image processing and the development
of computer-aided diagnostics. In recent years, deep Convolutional Neural Networks …

Deep learning (DL) has made significant strides in medical imaging. This review article
presents an in-depth analysis of DL applications in medical imaging, focusing on the …

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in
their size, appearance, and location makes the detection of polyps challenging. Moreover …

Recent developments in deep learning have enabled data-driven algorithms that can reach
human-level performance and beyond. The development and deployment of medical image …

Colon polyp is an important reference basis in the diagnosis of colorectal cancer (CRC). In
routine diagnosis, the polyp area is segmented from the colorectal enteroscopy image, and …

Segmentation of brain tumors is of great importance for patients in clinical diagnosis and
treatment. For this reason, experts try to identify border regions of special importance using …