HADS-Net fuses physics-informed texture features from EfficientNet-B3 with Sobel boundary features via cross-attention to reach 96.58% accuracy and 0.9978 macro ROC-AUC on the BUSI breast ultrasound dataset.
An explorative analysis of SVM classifier and ResNet50 architecture on African food classification,
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Vision Transformer with CLAHE preprocessing, two-stage fine-tuning, MixUp/CutMix, EMA, TTA, and attention rollout achieves 99.29% accuracy and 99.25% macro F1 on four-class brain tumor MRI classification from 7023 scans.
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HADS-Net:A Hybrid Attention-Augmented Dual-Stream Network with Physics-Informed Augmentation for Breast Ultrasound Image Classification
HADS-Net fuses physics-informed texture features from EfficientNet-B3 with Sobel boundary features via cross-attention to reach 96.58% accuracy and 0.9978 macro ROC-AUC on the BUSI breast ultrasound dataset.
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an interpretable vision transformer framework for automated brain tumor classification
Vision Transformer with CLAHE preprocessing, two-stage fine-tuning, MixUp/CutMix, EMA, TTA, and attention rollout achieves 99.29% accuracy and 99.25% macro F1 on four-class brain tumor MRI classification from 7023 scans.