Detection of Covid-19 using Mask R-CNN
2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023
; : 413-419, 2023.
Article
in English
| Scopus | ID: covidwho-2326495
ABSTRACT
Deep learning has been widely used to analyze radiographic pictures such as chest scans. These radiographic pictures include a wealth of information, including patterns and cluster-like formations, which aid in the discovery and conformance of COVID-19-like pandemics. The COVID-19 pandemic is wreaking havoc on global well-being and public health. Until present, more than 27 million confirmed cases have been recorded globally. Due to the increasing number of confirmed cases and issues with COVID-19 variants, fast and accurate categorization of healthy and infected individuals is critical for COVID-19 management and treatment. In medical image analysis and classification, artificial intelligence (AI) approaches in general, and region-based convolutional neural networks (CNNs) in particular, have yielded promising results. In this study, a deep Mask R-CNN architecture based on chest image classification is suggested for the diagnosis of COVID-19. An effective and reliable Mask R-CNN classification was difficult due to a lack of sufficient size and high-quality chest image datasets. These complications are addressed with Mask Region-based convolutional neural networks (R-CNNs) as a framework for detecting COVID-19 patients from chest pictures using an open-source dataset. First, the model was evaluated using 100 photos from the original processed dataset, and it was found to be accurate. The model was then validated against an independent dataset of COVID-19 X-ray pictures. The suggested model outperformed all other models in general and specifically when tested using an independent testing set. © 2023 IEEE.
COVID-19; MASK Region-Based Convolutional Neural Network Deep Learning.; Classification (of information); Computer aided diagnosis; Convolution; Convolutional neural networks; Deep learning; Image analysis; Image classification; Medical imaging; Architecture-based; Chest image; Convolutional neural network; Medical image analysis; Medical image classification; Neural network architecture; Region-based; Wealth of information; Well being
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
/
Prognostic study
Topics:
Variants
Language:
English
Journal:
2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023
Year:
2023
Document Type:
Article
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