Therefore, automated techniques are required to diagnose stomach infections from endoscopic images. The manual diagnosis process is time-consuming, requires many tests, and the availability of an expert doctor. The early diagnosis of stomach cancer is essential to reduce the mortality rate. Among all the types of cancer, stomach cancer is the most dangerous disease that spreads rapidly and needs to be diagnosed at an early stage. Several types of cancer sicken the human body and affect organs. The proposed method is the first study for the optimization of YOLO-based algorithms in the literature and makes a significant contribution to the detection accuracy.Ĭancer is the deadliest disease among all the diseases and the main cause of human mortality. In addition, the most comprehensive study is conducted by evaluating the performance of all existing models in the Scaled-YOLOv4 algorithm (YOLOv4s, YOLOv4m, YOLOV4-CSP, YOLOv4-P5, YOLOV4-P6 and YOLOv4-P7) on the novel SUN and PICCOLO polyp datasets. The proposed method improves the performance of the Scaled-YOLOv4 algorithm with an average of more than 3% increase in mAP and a more than 2% improvement in F1 value. The proposed method can be easily integrated into all YOLO algorithms such as YOLOv3, YOLOv4, Scaled-YOLOv4, YOLOv5, YOLOR and YOLOv7. We integrate the artificial bee colony algorithm (ABC) into the YOLO algorithm to optimize the hyper-parameters of YOLO-based algorithms. Here, we make significant improvements to object detection algorithms to improve the performance of CAD-based real-time polyp detection systems. In short, deep learning algorithms and applications have gained a critical role in CAD systems for real-time autonomous polyp detection. On the other hand, with the widespread use of deep learning algorithms in medical image analysis and the successful results in the analysis of colonoscopy images, especially in the early and accurate detection of polyps, these problems are eliminated in recent years. Most of these systems are based on traditional machine learning algorithms and their generalization ability, sensitivity and specificity are limited. Many computer-aided diagnosis systems (CADs) have been proposed for automatic polyp detection. Colonoscopy, considered the gold standard for CRC screening, allows immediate removal of polyps, which are precursors to CRC. We conclude this paper by providing a precise analysis of the gaps and trends discovered in the reviewed literature for future work.Ĭolorectal cancer (CRC) is one of the most common and malignant types of cancer worldwide. The survey covers benchmark dataset analysis, evaluation metrics, common challenges, standard methods of building polyp detectors and a review of the latest work in the literature. This paper presents a survey on recently proposed methods for detecting polyps from colonoscopy. Although various techniques have been presented recently, several key issues, such as the lack of enough training data, white light reflection, and blur affect the performance of such methods. Unidentified polyps can cause further complications and ultimately lead to colorectal cancer (CRC), one of the leading causes of cancer mortality. The motivation for researching this topic is that physicians miss polyps from time to time due to fatigue and lack of experience carrying out the procedure. 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 has been an active area of research in recent years.
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