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Kumar, Dinesh. Convolutional neural network for Covid-19 detection from X-ray images. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2021.
Convolutional neural network for Covid-19 detection from X-ray images
Name: Jain, Anju
Organization :
Guru Jambheshwar University of Science and Technology. Dept. of Computer Science & Engineering
Email :
anju_jain7@rediffmail.com
Name: Ratnoo, Saroj
Organization :
Guru Jambheshwar University of Science and Technology. Dept. of Computer Science & Engineering
Email :
ratnoo.saroj@gmail.com
Name: Kumar, Dinesh
Organization :
Guru Jambheshwar University of Science and Technology. Dept. of Computer Science & Engineering
Email :
dinesh_chutani@yahoo.com
LCSH:
Artificial intelligence
LCSH:
Image processing
LCSH:
COVID-19 (Disease)
-- Diagnosis
Abstract:
Covid-19 pandemic has crumbled the health systems of the
nations world over. In such a scenario, quick and accurate detection
of coronavirus infection plays an important role in timely referral of
physicians and control transmission of the disease. RT-PCR is the most
widely used method for identification of coronavirus disease 19
patients, but it is time consuming and takes two to three days to deliver
the report. Researchers around the world are looking for alternative
machine learning techniques including deep learning to assist the
medical experts for early Covid-19 disease diagnosis from medical
pictures such as X-ray films and CT scans. Since the facility for chest
X-rays is available even in smaller towns and is relatively less
expensive, it would be useful to design machine learning methods for
proving initial Covid-19 detection from chest X-rays to contain this
pandemic. Thus, in this work, we propose a Convolutional Neural
Network (CNN or ConvNet) for the finding of presence and absence of
Covid-19 disease. We compare the CNN model with traditional and
transfer learning-based machine learning algorithms. The proposed
CNN is accurate compared to the traditional machine learning
algorithms (KNN, SVM, DT etc.). The suggested CNN model is almost
as accurate as the classifiers based on transfer learning (such as
InceptionV3, VGG16 and ResNet50) despite being simple in terms of
number of parameters learnt. The CNN model takes less training time
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2021
Modified:
2024-06-11
Issued:
2024-06-10
บทความ/Article
application/pdf
BibliograpyCitation :
In IEEE Computer Society. 2021 Fourth International Conference on Computational Intelligence and Communication Technologies (CCICT 2021) (pp.100-104). Los Alamitos, CA : IEEE Computer Society
eng
©copyrights King Mongkut's University of Technology North Bangkok
RightsAccess:
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0.025329 วินาที
Jain, Anju
| Title | Contributor | Type |
|---|---|---|
| Convolutional neural network for Covid-19 detection from X-ray images
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Jain, Anju;Ratnoo, Saroj;Kumar, Dinesh | บทความ/Article |
Ratnoo, Saroj
| Title | Contributor | Type |
|---|---|---|
| Convolutional neural network for Covid-19 detection from X-ray images
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Jain, Anju;Ratnoo, Saroj;Kumar, Dinesh | บทความ/Article |
Kumar, Dinesh
| Title | Contributor | Type |
|---|---|---|
| Evaluation of antidiarrheal activity of ethanolic stem bark extract of Albizzia lebbeck Linn in rats
มหาวิทยาลัยสงขลานครินทร์. สำนักทรัพยากรการเรียนรู้คุณหญิงหลง อรรถกระวีสุนทร Balekar, Neelam;Kumar, Dinesh ;Jain, Pankaj Dixit;Nair, Veena | บทความ/Article | |
| Convolutional neural network for Covid-19 detection from X-ray images
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Jain, Anju;Ratnoo, Saroj;Kumar, Dinesh | บทความ/Article |