Evidence map›Paper›PMID 38737734›Full record

ArticleMobile networks and applications : MONET2023

LCCNN: a Lightweight Customized CNN-Based Distance Education App for COVID-19 Recognition.

Jiaji Wang, Suresh Chandra Satapathy, Shuihua Wang, Yudong Zhang

Abstract read
In one paragraph

Article in Mobile networks and applications : MONET, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

4 authors.

Jiaji Wang *School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH UK.
Suresh Chandra Satapathy *School of Computer Engineering, KIIT Deemed to University, Bhubaneswar, India.
Shuihua WangSchool of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH UK.
Yudong ZhangSchool of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH UK.ORCID 0000-0002-4870-1493

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the global epidemic, distance learning occupies an increasingly important place in teaching and learning because of its great potential. This paper proposes a web-based app that includes a proposed 8-layered lightweight, customized convolutional neural network (LCCNN) for COVID-19 recognition. Five-channel data augmentation is proposed and used to help the model avoid overfitting. The LCCNN achieves an accuracy of 91.78%, which is higher than the other eight state-of-the-art methods. The results show that this web-based app provides a valuable diagnostic perspective on the patients and is an excellent way to facilitate medical education. Our LCCNN model is explainable for both radiologists and distance education users. Heat maps are generated where the lesions are clearly spotted. The LCCNN can detect from CT images the presence of lesions caused by COVID-19. This web-based app has a clear and simple interface, which is easy to use. With the help of this app, teachers can provide distance education and guide students clearly to understand the damage caused by COVID-19, which can increase interaction with students and stimulate their interest in learning.

Indexed as

Convolutional neural networkCOVID-19Deep learningDistance educationMedical image diagnosisWeb app

Identifiers

PMID38737734
PMCPMC11083135

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.