Convolutional Neural Network (CNN) in medical image processing has lately received a lot of interest. Computer-aided polyp detection in gastrointestinal endoscopy has been the subject of research over the past few decades. However, despite significant advances, automatic polyp detection in real-time is still an unsolved problem. In this paper, we propose a Deep Learning method for reliable real-time polyp detection on endoscopic images and videos. We improve the performance of YOLOv8 model by modifying YOLOv8 model architecture with Ghost Convolution and Spatial and Channel Attention mechanisms (GhostAtt-YOLOv8). These techniques are integrated into the backbone network to enhance detection result. The proposed method is applied on Showa University and Nagoya University polyp database (SUN) dataset. Experimental results show that a better performance is archived with mAP@50 of 80.13% compared to the original YOLOv8, and FPS of our proposed model is 294, faster than original YOLOv8.