ArticleJournal of personalized medicine2022
Contrasting EfficientNet, ViT, and gMLP for COVID-19 Detection in Ultrasound Imagery.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Interpreting Venous and Arterial Ulcer Images Through the Grad-CAM Lens: Insights and Implications in CNN-Based Wound Image Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Constructing an artificial intelligence-assisted system for the assessment of gastroesophageal valve function based on the hill classification (with video).BMC medical informatics and decision making · 2025Article
- Preliminary exploratory study on differential diagnosis between benign and malignant peripheral lung tumors: based on deep learning networks.Frontiers in medicine · 2025Article
- Prediction of visceral pleural invasion of clinical stage I lung adenocarcinoma using thoracoscopic images and deep learning.Surgery today · 2024Article
- Perceptive SARS-CoV-2 End-To-End Ultrasound Video Classification through X3D and Key-Frames Selection.Bioengineering (Basel, Switzerland) · 2023Article
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5 authors.
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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
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