Elsevier

Applied Soft Computing

Volume 130, November 2022, 109656
Applied Soft Computing

CodnNet: A lightweight CNN architecture for detection of COVID-19 infection

https://doi.org/10.1016/j.asoc.2022.109656Get rights and content

Highlights

  • Adding Focus layer and modifying the pooling layer to make all features reusable.

  • An efficient depthwise separable convolution is used to improve the classification performance.

  • The proposed model can save bandwidth and reduce costs of storage for large datasets.

Abstract

The application of Convolutional Neural Network (CNN) on the detection of COVID-19 infection has yielded favorable results. However, with excessive model parameters, the CNN detection of COVID-19 is low in recall, highly complex in computation. In this paper, a novel lightweight CNN model, CodnNet is proposed for quick detection of COVID-19 infection. CodnNet builds a more effective dense connections based on DenseNet network to make features highly reusable and enhances interactivity of local and global features. It also uses depthwise separable convolution with large convolution kernels instead of traditional convolution to improve the range of receptive field and enhances classification performance while reducing model complexity. The 5-Fold cross validation results on Kaggle’s COVID-19 Dataset showed that CodnNet has an average precision of 97.9%, recall of 97.4%, F1score of 97.7%, accuracy of 98.5%, mAP of 99.3%, and mAUC of 99.7%. Compared to the typical CNNs, CodnNet with fewer parameters and lower computational complexity has achieved better classification accuracy and generalization performance. Therefore, the CodnNet model provides a good reference for quick detection of COVID-19 infection.

Keywords

DenseNet
Depthwise separable convolution
Lightweight CNN
COVID-19

Data availability

Data will be made available on request.

Cited by (0)

1

Co-first authors, contributed equally to this work.

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