Multimodality Imaging of COVID-19 Using Fine-Tuned Deep Learning Models.

Saleh Almuayqil, Sameh Abd El-Ghany, Abdulaziz Shehab

Abstract read

In one paragraph

Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Saleh AlmuayqilDepartment of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.ORCID 0000-0001-5696-7198

Sameh Abd El-GhanyDepartment of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.ORCID 0000-0001-6888-5114

Abdulaziz ShehabDepartment of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.ORCID 0000-0001-8610-7172

Funding

This research was funded by JOUF UNIVERSITY, grant number CV-35-41 CV-35-41

6 · The paper itself

Abstract

In the face of the COVID-19 pandemic, many studies have been undertaken to provide assistive recommendations to patients to help overcome the burden of the expected shortage in clinicians. Thus, this study focused on diagnosing the COVID-19 virus using a set of fine-tuned deep learning models to overcome the latency in virus checkups. Five recent deep learning algorithms (EfficientB0, VGG-19, DenseNet121, EfficientB7, and MobileNetV2) were utilized to label both CT scan and chest X-ray images as positive or negative for COVID-19. The experimental results showed the superiority of the proposed method compared to state-of-the-art methods in terms of precision, sensitivity, specificity, F1 score, accuracy, and data access time.

Indexed as

COVID-19deep learninghealthcaremultimodalitytransfer learning

Identifiers

PMID37046486

DOI10.3390/diagnostics13071268

PMCPMC10093688

What OpenQuestion holds

Textmetadata

LicenceCC BY

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.