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

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Abstract

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.

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The authors declare that the data supporting the findings of this study are included in this manuscript.

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Acknowledgements

Experimental computations were carried out on the calculation units in Artificial Intelligence and Big Data Application and Research Center, Erciyes University, Türkiye. Ethical permissions were granted by Acıbadem University, Türkiye with the permission number ATADEK-2021/02 and the decision number 2021-02/15.

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Authors and Affiliations

  1. Department of Gastroenterology, Acıbadem Hospital, Kayseri, Turkey

    Ahmet Karaman & Seymanur Coskun

  2. Department of Computer Engineering, Engineering Faculty, Erciyes University, Kayseri, Turkey

    Dervis Karaboga, Bahriye Akay, Alper Basturk, Ufuk Nalbantoglu & Omur Sahin

  3. Artificial Intelligence and Big Data Application and Research Center, Erciyes University, Kayseri, Turkey

    Dervis Karaboga, Ishak Pacal, Bahriye Akay, Alper Basturk, Ufuk Nalbantoglu & Omur Sahin

  4. Department of Computer Engineering, Engineering Faculty, Igdir University, Igdir, Turkey

    Ishak Pacal

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  1. Ahmet Karaman
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Karaman, A., Karaboga, D., Pacal, I. et al. Hyper-parameter optimization of deep learning architectures using artificial bee colony (ABC) algorithm for high performance real-time automatic colorectal cancer (CRC) polyp detection. Appl Intell 53, 15603–15620 (2023). https://doi.org/10.1007/s10489-022-04299-1

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