Robust real-time polyp detection system design based on YOLO algorithms by optimizing activation functions and hyper-parameters with artificial bee colony (ABC)

Colorectal cancer (CRC) is one of the most common cancer types with a high mortality rate. Colonoscopy is considered the gold standard in CRC screening, it also provides immediate removal of polyps, which are the precursors of CRC, significantly reducing CRC mortality. Polyps can be overlooked due to many factors and can progress to a fatal stage. Increasing the detection rate of missed polyps can be a turning point for CRC. Therefore, many traditional computer-aided detection (CAD) systems have been proposed, but the desired efficiency could not be obtained due to real-time detection or the limited sensitivity and specificity of the systems. In this article, we present a deep learning-based approach unlike traditional systems. This approach is basically based on 5th version of you only look once (YOLOv5) object detection algorithm and artificial bee colony (ABC) optimization algorithm. While models belonging to the YOLOv5 algorithm are used for polyp detection, the ABC algorithm is used to improve the performance of the models. The ABC algorithm is positioned to find the optimal activation functions and hyper-parameters for the YOLOv5 algorithm. The proposed method was performed on the novel Showa University and Nagoya University polyp database (SUN) dataset and PICCOLO white-light and narrow-band imaging colonoscopic dataset (PICCOLO). Experimental studies showed that the ABC algorithm successfully optimizes the YOLOv5 algorithm and offers much higher accuracy than the original YOLOv5 algorithm. The proposed method is far ahead of the existing methods in the literature in terms of speed and accuracy, with high performance in real-time polyp detection. This study is the first proposed method for optimization of activation functions and hyper-parameters for object detection algorithms.