An efficient real-time colonic polyp detection with YOLO algorithms trained by using negative samples and large datasets

Pacal I., Karaman A., KARABOĞA D., AKAY B., BAŞTÜRK A., Nalbantoglu U., ...More

Computers in Biology and Medicine, vol.141, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 141
  • Publication Date: 2022
  • Doi Number: 10.1016/j.compbiomed.2021.105031
  • Journal Name: Computers in Biology and Medicine
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, BIOSIS, Biotechnology Research Abstracts, CINAHL, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, Library, Information Science & Technology Abstracts (LISTA), MEDLINE
  • Keywords: Polyp detection, Deep learning, Medical image analysis, YOLOv4, YOLOv3, Scaled-YOLOv4, YOLOv4-CSP, Rectal cancer, Colon cancer, Real-time polyp detection, Negative samples, PICCOLO polyp dataset, SUN polyp dataset, Etis-Larib dataset, Convolutional neural networks, Colorectal cancer, COLORECTAL-CANCER
  • Kayseri University Affiliated: No


© 2021 Elsevier LtdColorectal cancer (CRC) is one of the common types of cancer with a high mortality rate. Colonoscopy is the gold standard for CRC screening and significantly reduces CRC mortality. However, due to many factors, the rate of missed polyps, which are the precursors of colorectal cancer, is high in practice. Therefore, many artificial intelligence-based computer-aided diagnostic systems have been presented to increase the detection rate of missed polyps. In this article, we present deep learning-based methods for reliable computer-assisted polyp detection. The proposed methods differ from state-of-the-art methods as follows. First, we improved the performances of YOLOv3 and YOLOv4 object detection algorithms by integrating Cross Stage Partial Network (CSPNet) for real-time and high-performance automatic polyp detection. Then, we utilized advanced data augmentation techniques and transfer learning to improve the performance of polyp detection. Next, for further improving the performance of polyp detection using negative samples, we substituted the Sigmoid-weighted Linear Unit (SiLU) activation functions instead of the Leaky ReLU and Mish activation functions, and Complete Intersection over Union (CIoU) as the loss function. In addition, we present a comparative analysis of these activation functions for polyp detection. We applied the proposed methods on the recently published novel datasets, which are the SUN polyp database and the PICCOLO database. Additionally, we investigated the proposed models for MICCAI Sub-Challenge on Automatic Polyp Detection in Colonoscopy dataset. The proposed methods outperformed the other studies in both real-time performance and polyp detection accuracy.