A timely diagnosis of coronavirus is critical in order to control the spread of the virus. To aid in this, we propose in this paper a deep learning-based approach for detecting coronavirus patients using ultrasound imagery. We propose to exploit the transfer learning of a EfficientNet model pre-trained on the ImageNet dataset for the classification of ultrasound images of suspected patients. In particular, we contrast the results of EfficentNet-B2 with the results of ViT and gMLP. Then, we show the results of the three models by learning from scratch, i.e., without transfer learning. We view the detection problem from a multiclass classification perspective by classifying images as COVID-19, pneumonia, and normal. In the experiments, we evaluated the models on a publically available ultrasound dataset. This dataset consists of 261 recordings (202 videos + 59 images) belonging to 216 distinct patients. The best results were obtained using EfficientNet-B2 with transfer learning. In particular, we obtained precision, recall, and F1 scores of 95.84%, 99.88%, and 24 97.41%, respectively, for detecting the COVID-19 class. EfficientNet-B2 with transfer learning presented an overall accuracy of 96.79%, outperforming gMLP and ViT, which achieved accuracies of 93.03% and 92.82%, respectively.

Contrasting EfficientNet, ViT, and gMLP for COVID-19 Detection in Ultrasound Imagery / Rahhal, M. M. A.; Bazi, Y.; Jomaa, R. M.; Zuair, M.; Melgani, F.. - In: JOURNAL OF PERSONALIZED MEDICINE. - ISSN 2075-4426. - ELETTRONICO. - 12:10(2022), pp. 170701-170717. [10.3390/jpm12101707]

Contrasting EfficientNet, ViT, and gMLP for COVID-19 Detection in Ultrasound Imagery

Bazi Y.;Melgani F.
2022-01-01

Abstract

A timely diagnosis of coronavirus is critical in order to control the spread of the virus. To aid in this, we propose in this paper a deep learning-based approach for detecting coronavirus patients using ultrasound imagery. We propose to exploit the transfer learning of a EfficientNet model pre-trained on the ImageNet dataset for the classification of ultrasound images of suspected patients. In particular, we contrast the results of EfficentNet-B2 with the results of ViT and gMLP. Then, we show the results of the three models by learning from scratch, i.e., without transfer learning. We view the detection problem from a multiclass classification perspective by classifying images as COVID-19, pneumonia, and normal. In the experiments, we evaluated the models on a publically available ultrasound dataset. This dataset consists of 261 recordings (202 videos + 59 images) belonging to 216 distinct patients. The best results were obtained using EfficientNet-B2 with transfer learning. In particular, we obtained precision, recall, and F1 scores of 95.84%, 99.88%, and 24 97.41%, respectively, for detecting the COVID-19 class. EfficientNet-B2 with transfer learning presented an overall accuracy of 96.79%, outperforming gMLP and ViT, which achieved accuracies of 93.03% and 92.82%, respectively.
2022
10
Rahhal, M. M. A.; Bazi, Y.; Jomaa, R. M.; Zuair, M.; Melgani, F.
Contrasting EfficientNet, ViT, and gMLP for COVID-19 Detection in Ultrasound Imagery / Rahhal, M. M. A.; Bazi, Y.; Jomaa, R. M.; Zuair, M.; Melgani, F.. - In: JOURNAL OF PERSONALIZED MEDICINE. - ISSN 2075-4426. - ELETTRONICO. - 12:10(2022), pp. 170701-170717. [10.3390/jpm12101707]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/372918
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