An Improved Detection Algorithm for Ischemic Stroke NCCT Based on YOLOv5.

Lifeng Zhang, Hongyan Cui, Anming Hu, Jiadong Li, Yidi Tang, Roy Elmer Welsch

Open access · goldAbstract read

In one paragraph

Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

0.8field-weighted citation impact, top 28% of its field

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed, 5 citations in OpenAlex.

Cerebral ischemia detection using deep learning techniques.Health information science and systems · 2025
Article

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 2 countries.

Lifeng ZhangSchool of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.ORCID 0000-0002-2377-0636

Hongyan CuiSchool of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Anming HuDepartment of Rehabilitation Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.ORCID 0000-0002-4124-1025

Jiadong LiSchool of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Yidi TangSchool of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Roy Elmer WelschSloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Beijing University of Posts and Telecommunications · CNCapital Medical University · CNMassachusetts Institute of Technology · US

Funding

National Natural Science Foundation of China 62171049On demand service intelligent network management and control prototype system 2020YFB1807805

6 · The paper itself

Abstract

Cerebral stroke (CS) is a heterogeneous syndrome caused by multiple disease mechanisms. Ischemic stroke (IS) is a subtype of CS that causes a disruption of cerebral blood flow with subsequent tissue damage. Noncontrast computer tomography (NCCT) is one of the most important IS detection methods. It is difficult to select the features of IS CT within computational image analysis. In this paper, we propose AC-YOLOv5, which is an improved detection algorithm for IS. The algorithm amplifies the features of IS via an NCCT image based on adaptive local region contrast enhancement, which then detects the region of interest via YOLOv5, which is one of the best detection algorithms at present. The proposed algorithm was tested on two datasets, and seven control group experiments were added, including popular detection algorithms at present and other detection algorithms based on image enhancement. The experimental results show that the proposed algorithm has a high accuracy (94.1% and 91.7%) and recall (85.3% and 88.6%) rate; the recall result is especially notable. This proves the excellent performance of the accuracy, robustness, and generalizability of the algorithm.

Indexed as

detection algorithmfeaturesischemic strokenoncontrast computer tomographyYOLOv5

Identifiers

PMID36359435

DOI10.3390/diagnostics12112591

PMCPMC9688968

OpenAlexW4307291665

What OpenQuestion holds

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.