Dec 20, 2022 · Some recent works have explored deep learning areas to automate metrics to assess endoscopy quality. These are especially critical in quantifying blind spots 20, 21.

The results of disease detection in endoscopic images over large datasets using deep learning and image detection technologies have shown promising outcomes in terms of accuracy, …

Nov 16, 2023 · This study presents an Endoscopic Image Analysis for Gastrointestinal Tract Disease Diagnosis using an inspired Algorithm with Deep Learning (EIAGTD-NIADL) technique.

Jan 26, 2023 · One key advantage of using explainable deep learning in the context of wireless endoscopic images is that it allows medical experts to identify and diagnose abnormalities and …

Jul 1, 2024 · This study developed a 13-layered convolutional neural network (CNN) model named as GINet for diagnosing GI diseases such as angiectasia, lymphangiectasia, GI …

Dec 26, 2021 · Computer-aided diagnosis (CAD) plays a vital role in the medical imaging field, allowing accurate results to be obtained in minimal time. This article proposes a new hybrid …

Nov 1, 2024 · Deep learning, particularly convolutional neural networks (CNNs), has revolutionized endoscopic image processing, significantly enhancing the efficiency and …

Jul 13, 2021 · Section 11.2 describes various deep learning architectures applied in endoscopic image analysis, with an emphasis on the typical techniques encountered in the current …

Apr 22, 2020 · We describe in this paper our deep learning-based approach for the EndoCV2020 challenge, which aims to detect and segment either artefacts or diseases in endoscopic images.

May 1, 2021 · We provided a comprehensive analysis of the deep learning methods built to tackle two distinct challenges in the gastrointestinal endoscopy: a) artefact detection and …