tetano
Editor, Senior Moderator
Sensors (Basel)
. 2022 Feb 5;22(3):1211.
doi: 10.3390/s22031211.
Chest X-ray Classification for the Detection of COVID-19 Using Deep Learning Techniques
Ejaz Khan[SUP] 1 [/SUP], Muhammad Zia Ur Rehman[SUP] 2 [/SUP], Fawad Ahmed[SUP] 3 [/SUP], Faisal Abdulaziz Alfouzan[SUP] 4 [/SUP], Nouf M Alzahrani[SUP] 5 [/SUP], Jawad Ahmad[SUP] 6 [/SUP]
Affiliations
Abstract
Recent technological developments pave the path for deep learning-based techniques to be used in almost every domain of life. The precision of deep learning techniques make it possible for these to be used in the medical field for the classification and detection of various diseases. Recently, the coronavirus (COVID-19) pandemic has put a lot of pressure on the health system all around the world. The diagnosis of COVID-19 is possible by PCR testing and medical imagining. Since COVID-19 is highly contagious, diagnosis using chest X-ray is considered safe in various situations. In this study, a deep learning-based technique is proposed to classify COVID-19 infection from other non-COVID-19 infections. To classify COVID-19, three different pre-trained models named EfficientNetB1, NasNetMobile and MobileNetV2 are used. The augmented dataset is used for training deep learning models while two different training strategies have been used for classification. In this study, not only are the deep learning model fine-tuned but also the hyperparameters are fine-tuned, which significantly improves the performance of the fine-tuned deep learning models. Moreover, the classification head is regularized to improve the performance. For the evaluation of the proposed techniques, several performance parameters are used to gauge the performance. EfficientNetB1 with regularized classification head outperforms the other models. The proposed technique successfully classifies four classes that include COVID-19, viral pneumonia, lung opacity, and normal, with an accuracy of 96.13%. The proposed technique shows superiority in terms of accuracy when compared with recent techniques present in the literature.
Keywords: COVID-19; chest X-rays; classification; deep learning; transfer learning.
. 2022 Feb 5;22(3):1211.
doi: 10.3390/s22031211.
Chest X-ray Classification for the Detection of COVID-19 Using Deep Learning Techniques
Ejaz Khan[SUP] 1 [/SUP], Muhammad Zia Ur Rehman[SUP] 2 [/SUP], Fawad Ahmed[SUP] 3 [/SUP], Faisal Abdulaziz Alfouzan[SUP] 4 [/SUP], Nouf M Alzahrani[SUP] 5 [/SUP], Jawad Ahmad[SUP] 6 [/SUP]
Affiliations
- PMID: 35161958
- DOI: 10.3390/s22031211
Abstract
Recent technological developments pave the path for deep learning-based techniques to be used in almost every domain of life. The precision of deep learning techniques make it possible for these to be used in the medical field for the classification and detection of various diseases. Recently, the coronavirus (COVID-19) pandemic has put a lot of pressure on the health system all around the world. The diagnosis of COVID-19 is possible by PCR testing and medical imagining. Since COVID-19 is highly contagious, diagnosis using chest X-ray is considered safe in various situations. In this study, a deep learning-based technique is proposed to classify COVID-19 infection from other non-COVID-19 infections. To classify COVID-19, three different pre-trained models named EfficientNetB1, NasNetMobile and MobileNetV2 are used. The augmented dataset is used for training deep learning models while two different training strategies have been used for classification. In this study, not only are the deep learning model fine-tuned but also the hyperparameters are fine-tuned, which significantly improves the performance of the fine-tuned deep learning models. Moreover, the classification head is regularized to improve the performance. For the evaluation of the proposed techniques, several performance parameters are used to gauge the performance. EfficientNetB1 with regularized classification head outperforms the other models. The proposed technique successfully classifies four classes that include COVID-19, viral pneumonia, lung opacity, and normal, with an accuracy of 96.13%. The proposed technique shows superiority in terms of accuracy when compared with recent techniques present in the literature.
Keywords: COVID-19; chest X-rays; classification; deep learning; transfer learning.