Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/159455
Title: Transfer learning-based approach for detecting COVID-19 ailment in lung CT scan
Authors: Arora, Vinay
Ng, Eddie Yin Kwee
Leekha, Rohan Singh
Darshan, Medhavi
Singh, Arshdeep
Keywords: Engineering::Mechanical engineering
Issue Date: 2021
Source: Arora, V., Ng, E. Y. K., Leekha, R. S., Darshan, M. & Singh, A. (2021). Transfer learning-based approach for detecting COVID-19 ailment in lung CT scan. Computers in Biology and Medicine, 135, 104575-. https://dx.doi.org/10.1016/j.compbiomed.2021.104575
Journal: Computers in Biology and Medicine
Abstract: This research work aims to identify COVID-19 through deep learning models using lung CT-SCAN images. In order to enhance lung CT scan efficiency, a super-residual dense neural network was applied. The experimentation has been carried out using benchmark datasets like SARS-COV-2 CT-Scan and Covid-CT Scan. To mark COVID-19 as positive or negative for the improved CT scan, existing pre-trained models such as XceptionNet, MobileNet, InceptionV3, DenseNet, ResNet50, and VGG (Visual Geometry Group)16 have been used. Taking CT scans with super resolution using a residual dense neural network in the pre-processing step resulted in improving the accuracy, F1 score, precision, and recall of the proposed model. On the dataset Covid-CT Scan and SARS-COV-2 CT-Scan, the MobileNet model provided a precision of 94.12% and 100% respectively.
URI: https://hdl.handle.net/10356/159455
ISSN: 0010-4825
DOI: 10.1016/j.compbiomed.2021.104575
Schools: School of Mechanical and Aerospace Engineering 
Rights: © 2021 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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