This systematic review and meta-analysis statistically appraises pooled data collected from 279 studies. It is the largest study to date examining the diagnostic accuracy of DL on medical...

Oct 3, 2023 · In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are …

In the field of medical image processing methods and analysis, fundamental information and state-of-the-art approaches with deep learning are presented in this paper.

Materials and Methods: In this systematic review, the PubMed database was searched for peer-reviewed studies of DL algorithms for im-age-based radiologic diagnosis that included external …

Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of DL algorithms to identify …

May 27, 2021 · In the past five years, many studies have focused on addressing this challenge. In this paper, we reviewed and summarized these recent studies to provide a comprehensive overview of …

Sep 21, 2023 · Deep learning (DL) has made significant strides in medical imaging. This review article presents an in-depth analysis of DL applications in medical imaging, focusing on the challenges, …

Apr 7, 2021 · Peer-reviewed studies that reported on the diagnostic accuracy of DL algorithms to identify pathology using medical imaging were included. Primary outcomes were measures of diagnostic …

This systematic review and meta-analysis statistically appraises pooled data collected from 279 studies. It is the largest study to date examining the diagnostic accuracy of DL on medical imaging.

We conducted a systematic search on PubMed and Scopus for original research articles leveraging deep learning for fusion of multimodality data.