A cVAE plus flow-matching model generates realistic complex-valued brain MRI that preserves phase coherence above 0.997 and yields synthetic data that trains abnormality classifiers to 0.880 AUROC, beating the 0.842 real-data baseline on fastMRI.
Niakan Kalhori
3 Pith papers cite this work. Polarity classification is still indexing.
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End-Net, a multiscale CNN with inception modules, claims superior accuracy on four-class neurological disorder MRI classification and includes online deployment.
EfficientNetB0 achieves the highest accuracy (95%) among five CNNs tested on multi-class brain tumor MRI classification, with notably better meningioma recall than shallower or custom models.
citing papers explorer
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A Deep Multiscale Neural Network for Accurate Neurological Disorder Detection from MRI Scans and Real-Time Web Deployment
End-Net, a multiscale CNN with inception modules, claims superior accuracy on four-class neurological disorder MRI classification and includes online deployment.
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Multi-Class Brain Tumor Classification Using Advanced Deep Learning Models: A Comparative Study
EfficientNetB0 achieves the highest accuracy (95%) among five CNNs tested on multi-class brain tumor MRI classification, with notably better meningioma recall than shallower or custom models.