Background and Aims Publicly available databases containing colonoscopic imaging data
are valuable resources for artificial intelligence (AI) research. Currently, little is known …

Purpose This work explores the potential of artificial intelligence (AI) for real-time colorectal
cancer (CRC) screening. We utilized a multi-house dataset to achieve a three-step approach …

Despite the successful demonstration of the deep learning architectures for Automatic
Colonoscopy Polyp Detection (ACPD), studies have argued upon human bias, and lack of …

The trade-off between speed and precision is a key step in the detection of small polyps in
wireless capsule endoscopy (WCE) images. In this paper, we propose a hybrid network of …

Small bowel polyps exhibit variations related to color, shape, morphology, texture, and size,
as well as to the presence of artifacts, irregular polyp borders, and the low illumination …

Object detection plays an important role to accelerate the medical diagnosis and automatic
polyp detection from colonoscopy images is one of the prominent examples. Visual …

The development of automated methods for analyzing medical images of colon cancer is
one of the main research fields. A colonoscopy is a medical treatment that enables a doctor …

Colorectal cancer (CRC) is one of the most common cancers worldwide. Early detection and
removal of colorectal polyps during colonoscopy are crucial for preventing such cancers …

The rise of object detection models has brought new breakthroughs to the development of
clinical decision support systems. However, in the field of gastrointestinal polyp detection …

Colorectal cancer (CRC) is a malignant condition that affects the colon or rectum, and it is
distinguished by abnormal cell growth in these areas. Colon polyps, which are …