A Lightweight Depthwise Separable Convolution Neural Network for Screening Covid-19 Infection from Chest CT and X-ray Images
18th Annual International Conference on Distributed Computing in Sensor Systems (Dcoss 2022)
; : 410-413, 2022.
Article
in English
| Web of Science | ID: covidwho-2070320
ABSTRACT
Because Covid-19 spreads swiftly in the community, an automatic detection system is required to prevent Covid-19 from spreading among humans as a rapid diagnostic tool. In this paper, we propose to employ Convolution Neural Networks to detect coronavirus-infected patients using Computed Tomography and X-ray images. In addition, we look into the transfer learning of a deep CNN model, DenseNet201 for detecting infection from CT and X-ray scans. Grid Search optimization is utilized to select ideal values for hyperparameters, while image augmentation is employed to increase the model's capacity to generalize. We further modify DenseNet architecture to incorporate a depthwise separable convolution for detecting coronavirus-infected patients utilizing CT and Xray images. Interestingly, all of the proposed models scored greater than 94% accuracy, which is equivalent to or higher than the accuracy of earlier deep learning models. Further, we demonstrate that depthwise separable convolution reduces the training time and computation complexity.
Full text:
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Collection:
Databases of international organizations
Database:
Web of Science
Language:
English
Journal:
18th Annual International Conference on Distributed Computing in Sensor Systems (Dcoss 2022)
Year:
2022
Document Type:
Article
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