Real time Automatic Polyp Detection in White light Endoscopy videos using a combination of YOLO and DeepSORT

Reddy, Jillella Sai Charan; Venkatesh, C.; Sinha, Saugata; Mazumdar, Srijan

doi:10.1109/pcems55161.2022.9807988

Cited by 8 publications

(2 citation statements)

References 8 publications

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“…Reddy et al [14] utilized the advanced YOLO-v4 detection model for the detection and monitoring of polyps during colonoscopy. The model's performance was evaluated based on the Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP) criteria at custom dataset.…”

Section: A Localizationmentioning

confidence: 99%

Automated Detection of Colorectal Polyp Utilizing Deep Learning Methods With Explainable AI

Ahamed,

Islam,

Nahiduzzaman

et al. 2024

IEEE Access

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Detecting colorectal polyps promptly and accurately is crucial in preventing the progression of colorectal cancer. These polyps cause severe conditions in the colon or rectum, presenting a significant diagnostic challenge. Traditional manual detection through medical imaging is not only bulky and prone to errors but also incurs substantial costs, requiring expert endoscopist. Inefficient detection and treatment can lead to critical health complications. Addressing these issues, we extensively employed various configurations of the state-of-the-art YOLOv8 (n-nano, s-small, m-medium, l-large, and x-extra-large) models for effective polyp localization. Complementing this, we proposed a novel TR-SE-Net model for segmentation, integrating Squeeze-and-Excite Networks (SE-Net) to elevate performance and real-time processing capabilities. The Kvasir-SEG dataset is utilized for training and testing models, supplemented by external validation CVC-ClinicDB, PolypGen, ETIS-LaribPolypDB, EDD 2020, and BKAI-IGH to confirm their efficacy in processing unseen, real-time data. This study delves into the interpretability of these models using explainable AI (XAI), such as eigen visualization for localization and heatmap analysis for segmentation. This exploration provides deeper insights into the decision-making processes of the models, thereby enhancing their reliability. Notably, the YOLOv8m model showcased remarkable prediction speed (approximately 16.61 ms) and excelled in precision (0.946), recall (0.771), F1-score (0.85), mAP50 (0.886), and mAP50-95 (0.695), catering to diverse clinical scenarios. The TR-SE-Net demonstrated significant improvements in segmentation performances, including DSC (0.8754), F2-score (0.8786), precision (0.9027), recall (0.8879), accuracy (0.9647), competitive mIoU (0.7961), FPS (54), parameters (27.27 million), and flops (10.59 GMac). Furthermore, A graphical Computer Aided Diagnosis (CAD) system developed utilizing both models can substantially reduce the miss rate because segmentation will assist in polyp detection or vice versa if localization fails. Conclusively, integrating these advanced computer-aided methods substantially enhances colonoscopy procedures by mitigating the risks of colorectal cancer.

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Section: A Localizationmentioning

confidence: 99%

Automated Detection of Colorectal Polyp Utilizing Deep Learning Methods With Explainable AI

Ahamed,

Islam,

Nahiduzzaman

et al. 2024

IEEE Access

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“…The application areas of YOLO are very wide. For example, in the medical field, related identification applications include cervical cancer [ 13 ], blood cells [ 14 ], colorectal cancer [ 15 ], venipuncture [ 16 ], etc., Applications also include the auxiliaries for hearing and visually impaired people [ 17 ] and sign language identification [ 18 ]. Furthermore, due to the influence of epidemics, there were many mask identification studies based on the use of different algorithms [ 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Clothing Style Recognition Using YOLOv5

Chang

Zhang

2022

Micromachines

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With the rapid development of artificial intelligence, much more attention has been paid to deep learning. However, as the complexity of learning algorithms increases, the needs of computation power of hardware facilities become more crucial. Instead of the focus being on computing devices like GPU computers, a lightweight learning algorithm could be the answer for this problem. Cross-domain applications of deep learning have attracted great interest amongst researchers in academia and industries. For beginners who do not have enough support with software and hardware, an open-source development environment is very helpful. In this paper, a relatively lightweight algorithm YOLOv5s is addressed, and the Google Colab is used for model training and testing. Based on the developed environment, many state-of-art learning algorithms can be studied for performance comparisons. To highlight the benefits of one-stage object detection algorithms, the recognition of clothing styles is investigated. The image samples are selected from datasets of fashion clothes and the web crawling of online stores. The image data are categorized into five groups: plaid; plain; block; horizontal; and vertical. Average precison, mean average precison, recall, F1-score, model size, and frame per second are the metrics used for performance validations. From the experimental outcomes, it shows that YOLOv5s is better than other learning algorithms in the recognition accuracy and detection speed.

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Using Linear Channel Attention to Enhance Real-Time Colonoscopy Object Detection

Le,

Dong,

Tang

et al. 2024

Lecture Notes in Computer Science

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Real time Automatic Polyp Detection in White light Endoscopy videos using a combination of YOLO and DeepSORT

Cited by 8 publications

References 8 publications

Automated Detection of Colorectal Polyp Utilizing Deep Learning Methods With Explainable AI

Automated Detection of Colorectal Polyp Utilizing Deep Learning Methods With Explainable AI

Deep Learning for Clothing Style Recognition Using YOLOv5

Using Linear Channel Attention to Enhance Real-Time Colonoscopy Object Detection

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