# Region-Based Automated Localization of Colonoscopy and Wireless Capsule Endoscopy Polyps
## 1 Introduction  
Colorectal cancer (CRC) is the third most lethal cancer both in men and women in the United States. It is estimated that about 14,600 new cases of colon and rectal cancer would be diagnosed in 2019, and it is expected to cause about 51,020 deaths. The risk of developing the cancer in men is about one in 22 (4.49%), and in women, it is about one in 24 (4.15%) [1]. CRC is prevalent in the large intestine of the lower gastrointestinal (GI) tract. It begins as a small, benign growth of glandular tissue on the inner lining of the colon that is called adenomatous polyps (adenomas). Over time, some of these polyps may become malignant and result in colon cancer. These cancerous cells can then grow into lymph vessels or blood vessels that can reach distant body parts and ultimately lead to death. The early detection and prevention of CRC is often done through regular screening [1]. Doctors can easily treat small polyps that have not spread. They can even remove them before they turn into a cancerous growth. Colonoscopy is the gold standard tool for colon screening [1]. During the screening, physicians look at the video samples extracted with a colonoscope, which is a long flexible tube that is mounted with small camera, and then put in through the rectum and into the colon. Colonoscopy may pose a risk of tear in the rectum wall or colon. There are other alternatives, which are less invasive to patients, such as Computed tomography (CT) colonography and wireless capsule endoscopy. CT colonography uses X-ray equipment for the examination. Due to limited resolution,CT colonography has a lower detection rate on small lesions [2]. Wireless capsule endoscopy (WCE) is another screening methodology of the colon to detect colon polyps. This procedure uses a capsule that has a small wireless video camera to capture the GI tract and transmit the frames to a recorder that is worn on a belt around the waist. The capsule is swallowed, and screening is performed from the observed recordings. The only limitation is the huge time consumption for the screening, since the recorded videos can be 8 h long. Despite clinicians' skills, some polyps are missed due to their size variations and some being hidden in the folds of colon walls. This may lead to the detection of lesions as non-cancerous, resulting in misdiagnosis and leading to a lower survival rate of less than 10% [3]. Advances in medical imaging analysis have led to cooperation between clinicians and computer researchers to automate detection, analysis, and validation with computer-aided support for CRC diagnosis. The automated detection of polyps in colonoscopy and capsule endoscopy videos has been an active area of research. Various methodologies have been proposed for automatic polyp detection in colonoscopy and WCE. Most of the approaches were based on handcrafted feature descriptors, including texture, color, and shape [4; 5; 6; 7]. In recent years, deep learning approaches have been incorporated to further enhance the accuracy of detection and segmentation [8; 9; 10]. Since colonoscopy is the gold standard for polyp screening, more literature with colonoscopy can be found compared to wireless endoscopy. There are various challenges in the automated detection of polyps. As shown in Figures 1 and 2, polyps appear in different sizes, shapes, textures, and color. Their endoscopic appearance can be similar to protruded lesions, flat elevated lesions, and flat lesions. The images even have noisy background with bleeding and endoluminal folds, which suppresses the accuracy of the detection process. In the past few years, there have been many proposals made by various researchers to tackle the polyp detection challenge. A study with a common validation framework was provided as a part of the Medical Image Computing and Computer Assisted Intervention (MICCAI) sub-challenge on automatic polyp detection [21]. This has provided a consistent evaluation to assist polyp detection in colonoscopy images.