Hyper-parameter optimization of deep learning architectures using artificial bee colony (ABC) algorithm for high performance real-time automatic colorectal cancer (CRC) polyp detection

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

Applied Intelligence, vol.53, no.12, pp.15603-15620, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 53 Issue: 12
  • Publication Date: 2023
  • Doi Number: 10.1007/s10489-022-04299-1
  • Journal Name: Applied Intelligence
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, ABI/INFORM, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, Educational research abstracts (ERA), INSPEC, Library, Information Science & Technology Abstracts (LISTA), zbMATH
  • Page Numbers: pp.15603-15620
  • Keywords: Colorectal cancer, Deep learning, Hyper-parameter optimization, Artificial bee colony, Real-time polyp detection, YOLOv4, CONVOLUTIONAL NEURAL-NETWORKS
  • Kayseri University Affiliated: No


© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Colorectal cancer (CRC) is one of the most common and malignant types of cancer worldwide. Colonoscopy, considered the gold standard for CRC screening, allows immediate removal of polyps, which are precursors to CRC. Many computer-aided diagnosis systems (CADs) have been proposed for automatic polyp detection. Most of these systems are based on traditional machine learning algorithms and their generalization ability, sensitivity and specificity are limited. 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. In short, deep learning algorithms and applications have gained a critical role in CAD systems for real-time autonomous polyp detection. Here, we make significant improvements to object detection algorithms to improve the performance of CAD-based real-time polyp detection systems. We integrate the artificial bee colony algorithm (ABC) into the YOLO algorithm to optimize the hyper-parameters of YOLO-based algorithms. The proposed method can be easily integrated into all YOLO algorithms such as YOLOv3, YOLOv4, Scaled-YOLOv4, YOLOv5, YOLOR and YOLOv7. 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. 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 is the first study for the optimization of YOLO-based algorithms in the literature and makes a significant contribution to the detection accuracy.