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, Ahmet; Karaboğa, Derviş; Pacal, Ishak; Akay, Bahriye; Baştürk, Alper; Nalbantoğlu, Ufuk; Coskun, Seymanur; Sahin, Omur

doi:10.1007/s10489-022-04299-1

Cited by 39 publications

(11 citation statements)

References 51 publications

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“…Experimental validation of the proposed method’s polyp detection performance was conducted using public datasets. In the work by Karaman and Pacal [ 25 ], they enhance YOLO-based object detection through the utilisation of the ABC algorithm. This optimisation results in a 3% enhancement in real-time polyp detection using YOLO-V5 on the SUN and PICCOLO datasets.…”

Section: Related Studiesmentioning

confidence: 99%

Polypoid Lesion Segmentation Using YOLO-V8 Network in Wireless Video Capsule Endoscopy Images

Sahafi,

Koulaouzidis,

Lalinia

2024

Diagnostics

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Gastrointestinal (GI) tract disorders are a significant public health issue. They are becoming more common and can cause serious health problems and high healthcare costs. Small bowel tumours (SBTs) and colorectal cancer (CRC) are both becoming more prevalent, especially among younger adults. Early detection and removal of polyps (precursors of malignancy) is essential for prevention. Wireless Capsule Endoscopy (WCE) is a procedure that utilises swallowable camera devices that capture images of the GI tract. Because WCE generates a large number of images, automated polyp segmentation is crucial. This paper reviews computer-aided approaches to polyp detection using WCE imagery and evaluates them using a dataset of labelled anomalies and findings. The study focuses on YOLO-V8, an improved deep learning model, for polyp segmentation and finds that it performs better than existing methods, achieving high precision and recall. The present study underscores the potential of automated detection systems in improving GI polyp identification.

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Section: Related Studiesmentioning

confidence: 99%

Polypoid Lesion Segmentation Using YOLO-V8 Network in Wireless Video Capsule Endoscopy Images

Sahafi,

Koulaouzidis,

Lalinia

2024

Diagnostics

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“…For example, Pacal et al 5 employed Scaled-YOLOv4 with different backbones such as CSPNet, ResNet, DarkNet, and Transformer for polyp detection. Karaman et al 26 integrated the artificial bee colony algorithm (ABC) into the YOLO baselines to optimize the hyper-parameters and conducted comprehensive studies on Scaled-YOLOv4. Furthermore, they utilized the ABC algorithm to find the optimal activation functions and hyper-parameters for the YOLOv5 detector and successfully obtained much higher performance in real-time polyp detection 27 .…”

Section: Related Workmentioning

confidence: 99%

Self-supervised representation learning using feature pyramid siamese networks for colorectal polyp detection

Gan,

Jin,

et al. 2023

Sci Rep

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Colorectal cancer is a leading cause of cancer-related deaths globally. In recent years, the use of convolutional neural networks in computer-aided diagnosis (CAD) has facilitated simpler detection of early lesions like polyps during real-time colonoscopy. However, the majority of existing techniques require a large training dataset annotated by experienced experts. To alleviate the laborious task of image annotation and utilize the vast amounts of readily available unlabeled colonoscopy data to further improve the polyp detection ability, this study proposed a novel self-supervised representation learning method called feature pyramid siamese networks (FPSiam). First, a feature pyramid encoder module was proposed to effectively extract and fuse both local and global feature representations among colonoscopic images, which is important for dense prediction tasks like polyp detection. Next, a self-supervised visual feature representation containing the general feature of colonoscopic images is learned by the siamese networks. Finally, the feature representation will be transferred to the downstream colorectal polyp detection task. A total of 103 videos (861,400 frames), 100 videos (24,789 frames), and 60 videos (15,397 frames) in the LDPolypVideo dataset are used to pre-train, train, and test the performance of the proposed FPSiam and its counterparts, respectively. The experimental results have illustrated that our FPSiam approach obtains the optimal capability, which is better than that of other state-of-the-art self-supervised learning methods and is also higher than the method based on transfer learning by 2.3 mAP and 3.6 mAP for two typical detectors. In conclusion, FPSiam provides a cost-efficient solution for developing colorectal polyp detection systems, especially in conditions where only a small fraction of the dataset is labeled while the majority remains unlabeled. Besides, it also brings fresh perspectives into other endoscopic image analysis tasks.

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“…CNNs are very useful for image classification and segmentation due to their ability to extract features and cope with significant variations . Deep learning applications such as colonic polyp detection for colorectal cancer, flying object detection, high throughput prediction, and signal power estimation are employed by health, defense, and communication systems (Karaman et al;Sharma et al,2018;Murshid et al, 2017;Egi & Eyceurt, 2022).…”

Section: Introductionmentioning

confidence: 99%

Kalabalık Kamu Alanları için YOLO V7 ve Bilgisayar Görmesi Temelli Maske Giyim Uyarı Sistemi

Egi

2023

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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The impact of Covid 19 cases is increasing worldwide due to not complying with social distancing and mask-wearing rules in congested areas such as hospitals, schools, and malls where people have to be together. Although the authorities have taken various precautions to prevent not wearing masks, it is challenging to inspect masks in crowded areas. People who do not wear masks can be unnoticed by visual inspections, which is a critical factor in the increase of the epidemic. This study aims to create an Artificial Intelligence (AI) based mask inspection system with the YOLO V7 deep learning method to ensure that overcrowded public areas are protected from the Covid-19 epidemic.

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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

Cited by 39 publications

References 51 publications

Polypoid Lesion Segmentation Using YOLO-V8 Network in Wireless Video Capsule Endoscopy Images

Polypoid Lesion Segmentation Using YOLO-V8 Network in Wireless Video Capsule Endoscopy Images

Self-supervised representation learning using feature pyramid siamese networks for colorectal polyp detection

Kalabalık Kamu Alanları için YOLO V7 ve Bilgisayar Görmesi Temelli Maske Giyim Uyarı Sistemi

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