Given the increased interest in utilizing artificial intelligence as an assistive tool in the
medical sector, colorectal polyp detection and classification using deep learning techniques …

The proposed work introduces a novel, lightweight feature fusion network model based on
the attention mechanism to address the issues of high time consumption and poor …

Underwater target detection holds a noteworthy role in the field of marine exploration.
However, it is difficult to extract useful feature information from blurred images with complex …

Gastrointestinal tract disorders, including colorectal cancer (CRC), impose a significant
health burden in Europe, with rising incidence rates among both young and elderly …

Detecting colorectal polyps promptly and accurately is crucial in preventing the progression
of colorectal cancer. These polyps cause severe conditions in the colon or rectum …

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide.
Colonoscopy is the primary method to prevent CRC. However, traditional polyp detection …

Endoscopy, a pervasive instrument for the diagnosis and treatment of hollow anatomical
structures, conventionally necessitates the arduous manual scrutiny of seasoned medical …

The significant challenge in detecting polyps in wireless capsule endoscopy and
colonoscopy images lies in identifying the small ones. This detection task exhibits variability …

In the rapidly evolving realm of machine learning, the integration of the Open Neural
Network Exchange (ONNX) has become increasingly significant, particularly in image …

Mathematical morphology and convolution operators are two different methods to extract the
characteristics and structures of images. Over the past decades, Deep Convolutional Neural …