Benchmarking Deep Transfer Learning Models for COVID-19 Lung Ultrasound Detection
A systematic benchmarking study demonstrates that DenseNet-121 paired with the Adam optimizer achieves exceptional diagnostic accuracy in detecting COVID-19 from ...
A systematic benchmarking study demonstrates that DenseNet-121 paired with the Adam optimizer achieves exceptional diagnostic accuracy in detecting COVID-19 from ...
A compact residual network called MSERNet removes JPEG compression artifacts from ovarian and carotid ultrasound images while preserving tissue textures ...
Researchers report that a transfer learning framework called TL-CDVD reached an average accuracy of 88.8 percent and an F1-score of ...
A feature attention mechanism embedded in a deep network restored apple sugar-prediction accuracy across three harvest years using only a ...
A fully convolutional network with layer-wise fine-tuning classifies malware families with over 99% accuracy across three datasets and processes obfuscated ...
A fine-tuned VGG16 network achieved 84.0 percent subset accuracy and an 85.2 percent micro-averaged F1-score on a held-out test set ...
A systematic review of 46 studies finds deep learning promising for post-earthquake structural damage assessment, but weak generalisation evidence and ...
A systematic comparison of nine CNN architectures on label-free multimodal microscopy images of colonic tissue found ResNet50 with partial fine-tuning ...
A deep learning model called DeepScope classifies microscopic images of unincubated water samples in seconds, with an estimated per-test cost ...
A transfer learning framework classified neonatal seizures from EEG topographic maps, with ResNet-18 reaching 97.1 percent subject-wise accuracy on unseen ...
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© 2026 Scienmag - Science Magazine