COVID-LiteNet: A lightweight CNN based network for COVID-19 detection using X-ray images
15th International Conference on Developments in eSystems Engineering, DeSE 2023
; 2023-January:363-368, 2023.
Article
in English
| Scopus | ID: covidwho-2327175
ABSTRACT
To restrict the virus's transmission in the pandemic and lessen the strain on the healthcare industry, computer-assisted diagnostics for the accurate and speedy diagnosis of coronavirus illness (COVID-19) has become a prerequisite. Compared to other types of imaging and detection, chest X-ray imaging (CXR) provides several advantages. Healthcare practitioners may profit from any technology instrument providing quick and accurate COVID-19 infection detection. COVID-LiteNet is a technique suggested in this paper that combines white balance with Contrast Limited Adaptive Histogram Equalization (CLAHE) and a convolutional neural network (CNN). White balance is employed as an image pre-processing step in this approach, followed by CLAHE, to improve the visibility of CXR images, and CNN is trained using sparse categorical cross-entropy for image classification tasks and gives the smaller parameters file size, i.e., 2.24 MB. The suggested COVID-LiteNet technique produced better results than vanilla CNN with no pre-processing. The proposed approach outperformed several state-of-the-art methods with a binary classification accuracy of 98.44 percent and a multi-class classification accuracy of 97.50 percent. COVID-LiteNet, the suggested technique, outperformed the competition on various performance parameters. COVID-LiteNet may help radiologists discover COVID-19 patients from CXR pictures by providing thorough model interpretations, cutting diagnostic time significantly. © 2023 IEEE.
Chest X-Ray; Computer Vision; convolutional neural network; COVID-19 Detection; COVID-LiteNet; Deep Learning; Machine Learning; Classification (of information); Computer aided diagnosis; Convolution; Convolutional neural networks; Health care; Image classification; Image enhancement; Learning systems; Adaptive histograms; Classification accuracy; Histogram equalizations; Machine-learning; White balance; COVID-19
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Randomized controlled trials
Language:
English
Journal:
15th International Conference on Developments in eSystems Engineering, DeSE 2023
Year:
2023
Document Type:
Article
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