Precision enhancement in wireless capsule endoscopy: a novel transformer-based approach for real-time video object detection DOI Creative Commons
Tsedeke Temesgen Habe,

Keijo Haataja,

Pekka Toivanen

и другие.

Frontiers in Artificial Intelligence, Год журнала: 2025, Номер 8

Опубликована: Апрель 30, 2025

Wireless Capsule Endoscopy (WCE) enables non-invasive imaging of the gastrointestinal tract but generates vast video data, making real-time and accurate abnormality detection challenging. Traditional methods struggle with uncontrolled illumination, complex textures, high-speed processing demands. This study presents a novel approach using Real-Time Detection Transformer (RT-DETR), transformer-based object model, specifically optimized for WCE analysis. The model captures contextual information between frames handles variable image conditions. It was evaluated Kvasir-Capsule dataset, performance assessed across three RT-DETR variants: Small (S), Medium (M), X-Large (X). RT-DETR-X achieved highest precision. RT-DETR-M offered practical trade-off accuracy speed, while RT-DETR-S processed at 270 FPS, enabling performance. All models demonstrated improved computational efficiency compared to baseline methods. framework significantly enhances precision in WCE. Its clinical potential lies supporting faster more diagnosis. Future work will focus on further optimization deployment endoscopic analysis systems.

Язык: Английский

Precision enhancement in wireless capsule endoscopy: a novel transformer-based approach for real-time video object detection DOI Creative Commons
Tsedeke Temesgen Habe,

Keijo Haataja,

Pekka Toivanen

и другие.

Frontiers in Artificial Intelligence, Год журнала: 2025, Номер 8

Опубликована: Апрель 30, 2025

Wireless Capsule Endoscopy (WCE) enables non-invasive imaging of the gastrointestinal tract but generates vast video data, making real-time and accurate abnormality detection challenging. Traditional methods struggle with uncontrolled illumination, complex textures, high-speed processing demands. This study presents a novel approach using Real-Time Detection Transformer (RT-DETR), transformer-based object model, specifically optimized for WCE analysis. The model captures contextual information between frames handles variable image conditions. It was evaluated Kvasir-Capsule dataset, performance assessed across three RT-DETR variants: Small (S), Medium (M), X-Large (X). RT-DETR-X achieved highest precision. RT-DETR-M offered practical trade-off accuracy speed, while RT-DETR-S processed at 270 FPS, enabling performance. All models demonstrated improved computational efficiency compared to baseline methods. framework significantly enhances precision in WCE. Its clinical potential lies supporting faster more diagnosis. Future work will focus on further optimization deployment endoscopic analysis systems.

Язык: Английский

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