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Top1. Introduction
Cancer is one of the most dangerous health threats to human life. It is ranked as the second most common cause of death after atherosclerosis and heart disease. Therefore, cancer is a serious challenge to most stakeholders. According to the National Cancer Institute (NIH), approximately 606,520 people were expected to die, and 1,806,950 million new cases of cancer were diagnosed in the United States in 2020. Among the cancer diseases, Gastrointestinal (GI) cancer affects the digestive tract starting from the esophagus to the anus, which is classified as upper malignancy (i.e., esophagus and stomach) and lower malignancy (i.e. include colon and rectal cancer), representing the third most dangerous GI cancer (Bray et al., 2018). Gastrointestinal cancers (GI) are responsible for one cancer case among four cases and one death case among three cases, as per WHO statistics. The statistics show more than 3.5 million GI cancer cases are officially registered in 2018 worldwide. In particular, colon cancer, also known as colorectal cancer, is a serious cancer that has a high incidence and mortality rate in developed countries. Colorectal cancer (CRC) is the third most common cancer in both men and women in the United States (Vijeta et al., 2020). In addition, CRC is the most diagnosed cancer in males and the third most diagnosed disease in females in Saudi society, representing 13% of diagnosed cancers (Alyabsi et al., 2021). Therefore, the early diagnosis of CRC is highly recommended to detect the polyps in the body before cancer develops and spreads to other normal cells. As known medically, cancer grows slowly with no significant symptoms. Thus, early diagnosis helps greatly to optimize treatment (Liu et al., 2016a). There are different biomarkers that may help physicians to detect cancer at the early stages such as radiographic, physiologic, molecular, and histologic applications (Liu et al., 2016b). For instance, endoscopy is widely used to diagnose cancer. However, the diagnosis results are not always accurate and it may be an uncomfortable procedure for patients (Lan et al., 2019). Alternatively, the Wireless Capsule Endoscopy (WCE) is swallowed by the patient to capture a high-resolution video with high frame rates for the whole gastrointestinal tract from the esophagus to the anus. However, such diagnostic videos must be processed and analyzed manually by physicians. This may be erroneous and time-consuming as the tumor might not be obvious in most of the frames. This problem has been overcome through the use of Computer Aided Diagnostic (CAD) systems. CAD systems play a crucial role in the early detection of cancer (Inbarani et al., 2020; Owais et al., 2019; Jothi et al., 2019, 2013; Banu et al., 2017; Abd El-Salam et al., 2014; Anter et al., 2013, 2014). The evolution of CAD systems for CRC can be traced from traditional models that require complex a priori mathematical knowledge (Tamai et al., 2017) to advanced machine learning (ML)-based systems (Min et al., 2019; Wang et al., 2019; Nadimi et al., 2020; Ozawa et al., 2020; Zeng et al., 2020; Qadir et al., 2020; Fati et al., 2022; Hassanien et al., 2014; Aziz et al., 2013) that can outperform human levels of accuracy. For example, in (Min et al., 2019), by analysing the colours of the lesions, the authors created a computer-aided diagnostic (CAD) system based on linked colour imaging (LCI) pictures to predict the histology outcomes of polyps. Wang et al. (2019) presented a revolutionary anchor-free polyp detector in this research. It is quicker than the anchor-based approach while providing superior performance. In addition to better performance, the authors eliminate the time-consuming process of manually fine-tuning anchor-related hyper-parameters. Nadimi et al. (2020) designed a convolutional neural network (CNN) for the autonomous identification of colorectal polyps in pictures taken during wireless colon capsule endoscopy, with the risk of malignant progression to colorectal cancer. The suggested CNN is an enhanced version of ZF-Net that employs transfer learning, pre-processing, and data augmentation. The authors then used CNN as the foundation for a Faster R-CNN to locate colorectal polyps in the images. In (Ozawa et al., 2020), a Single Shot MultiBox Detector deep convolutional neural network (CNN) architecture is used. The trained CNN recognised colorectal polyps (CP) with astonishing accuracy and speed, especially when the CP were tiny, which may help decrease missed CP if used during colonoscopy. To capture structural patterns in human colon optical coherence tomography (OCT) pictures, Zeng et al. (2020) created a convolutional neural network. The trained network correctly identified patterns that distinguish between normal and cancerous colorectal tissue. The experimental diagnoses indicated by the PR-OCT method were statistically analysed and compared to known histologic findings. Qadir et al. (2020) introduced a novel polyp identification framework that may be used in conjunction with any object detection algorithm to incorporate temporal information and improve overall polyp detection performance in colonoscopy movies. In its current condition, the suggested technique combines individual frame analysis with temporal video analysis to reach the ultimate conclusion. Fati et al. (2022) described a collection of multi-method methods for early diagnosis of endoscopic pictures from a lower GI dataset. All of the suggested algorithms produced extremely accurate diagnosis findings in diagnosing endoscopic pictures of lower gastrointestinal illness datasets.