tetano
Editor, Senior Moderator
Soft comput
. 2022 Jan 28;1-12.
doi: 10.1007/s00500-021-06579-3. Online ahead of print.
COVID-19 detection from chest x-ray using MobileNet and residual separable convolution block
V Santhosh Kumar Tangudu[SUP] 1 [/SUP], Jagadeesh Kakarla[SUP] 1 [/SUP], Isunuri Bala Venkateswarlu[SUP] 1 [/SUP]
Affiliations
Abstract
A newly emerged coronavirus disease affects the social and economical life of the world. This virus mainly infects the respiratory system and spreads with airborne communication. Several countries witness the serious consequences of the COVID-19 pandemic. Early detection of COVID-19 infection is the critical step to survive a patient from death. The chest radiography examination is the fast and cost-effective way for COVID-19 detection. Several researchers have been motivated to automate COVID-19 detection and diagnosis process using chest x-ray images. However, existing models employ deep networks and are suffering from high training time. This work presents transfer learning and residual separable convolution block for COVID-19 detection. The proposed model utilizes pre-trained MobileNet for binary image classification. The proposed residual separable convolution block has improved the performance of basic MobileNet. Two publicly available datasets COVID5K, and COVIDRD have considered for the evaluation of the proposed model. Our proposed model exhibits superior performance than existing state-of-art and pre-trained models with 99% accuracy on both datasets. We have achieved similar performance on noisy datasets. Moreover, the proposed model outperforms existing pre-trained models with less training time and competitive performance than basic MobileNet. Further, our model is suitable for mobile applications as it uses fewer parameters and lesser training time.
. 2022 Jan 28;1-12.
doi: 10.1007/s00500-021-06579-3. Online ahead of print.
COVID-19 detection from chest x-ray using MobileNet and residual separable convolution block
V Santhosh Kumar Tangudu[SUP] 1 [/SUP], Jagadeesh Kakarla[SUP] 1 [/SUP], Isunuri Bala Venkateswarlu[SUP] 1 [/SUP]
Affiliations
- PMID: 35106060
- PMCID: PMC8794607
- DOI: 10.1007/s00500-021-06579-3
Abstract
A newly emerged coronavirus disease affects the social and economical life of the world. This virus mainly infects the respiratory system and spreads with airborne communication. Several countries witness the serious consequences of the COVID-19 pandemic. Early detection of COVID-19 infection is the critical step to survive a patient from death. The chest radiography examination is the fast and cost-effective way for COVID-19 detection. Several researchers have been motivated to automate COVID-19 detection and diagnosis process using chest x-ray images. However, existing models employ deep networks and are suffering from high training time. This work presents transfer learning and residual separable convolution block for COVID-19 detection. The proposed model utilizes pre-trained MobileNet for binary image classification. The proposed residual separable convolution block has improved the performance of basic MobileNet. Two publicly available datasets COVID5K, and COVIDRD have considered for the evaluation of the proposed model. Our proposed model exhibits superior performance than existing state-of-art and pre-trained models with 99% accuracy on both datasets. We have achieved similar performance on noisy datasets. Moreover, the proposed model outperforms existing pre-trained models with less training time and competitive performance than basic MobileNet. Further, our model is suitable for mobile applications as it uses fewer parameters and lesser training time.