Evidence map›Paper›PMID 35465215›Full record

ArticleInternational journal of imaging systems and technology2022

COVID-opt-aiNet: A clinical decision support system for COVID-19 detection.

Summrina Kanwal, Faiza Khan, Sultan Alamri, Kia Dashtipur, Mandar Gogate

Open access · greenAbstract read
In one paragraph

Article in International journal of imaging systems and technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.9field-weighted citation impact, top 16% 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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
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  6. COVID-opt-aiNet: A clinical decision support system for COVID-19 detection.International journal of imaging systems and technology · 2022
    Article
4 · The record

Corrections and comments

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

5 authors at 3 institutions in 3 countries.

Summrina KanwalDepartment of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia.
Faiza KhanFaculty of Computing Riphah International University Islamabad Pakistan.ORCID https://orcid.org/0000-0002-3031-7506
Sultan AlamriDepartment of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia.
Kia DashtipurJames Watt School of Engineering University of Glasgow Glasgow UK.
Mandar GogateSchool of Computing, Merchiston Campus, Edinburgh Napier University Edinburgh UK.
Saudi Electronic University · SAEdinburgh Napier University · GBRiphah International University · PK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronavirus disease (COVID-19) has had a major and sometimes lethal effect on global public health. COVID-19 detection is a difficult task that necessitates the use of intelligent diagnosis algorithms. Numerous studies have suggested the use of artificial intelligence (AI) and machine learning (ML) techniques to detect COVID-19 infection in patients through chest X-ray image analysis. The use of medical imaging with different modalities for COVID-19 detection has become an important means of containing the spread of this disease. However, medical images are not sufficiently adequate for routine clinical use; there is, therefore, an increasing need for AI to be applied to improve the diagnostic performance of medical image analysis. Regrettably, due to the evolving nature of the COVID-19 global epidemic, the systematic collection of a large data set for deep neural network (DNN)/ML training is problematic. Inspired by these studies, and to aid in the medical diagnosis and control of this contagious disease, we suggest a novel approach that ensembles the feature selection capability of the optimized artificial immune networks (opt-aiNet) algorithm with deep learning (DL) and ML techniques for better prediction of the disease. In this article, we experimented with a DNN, a convolutional neural network (CNN), bidirectional long-short-term memory, a support vector machine (SVM), and logistic regression for the effective detection of COVID-19 in patients. We illustrate the effectiveness of this proposed technique by using COVID-19 image datasets with a variety of modalities. An empirical study using the COVID-19 image dataset demonstrates that the proposed hybrid approaches, named COVID-opt-aiNet, improve classification accuracy by up to 98%-99% for SVM, 96%-97% for DNN, and 70.85%-71% for CNN, to name a few examples. Furthermore, statistical analysis ensures the validity of our proposed algorithms. The source code can be downloaded from Github: https://github.com/faizakhan1925/COVID-opt-aiNet.

Indexed as

bidirectional long‐short‐term memoryclinical decision support systemconvolution neural networkCOVID‐19deep learning neural networkfeature selectionoptimized artificial immune networksupport vector machine

Identifiers

PMID35465215
PMCPMC9015255
OpenAlexW4206562637

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.