Hybrid CNN-Transformer AI Classifies Melanoma with 98.55% Accuracy on ISIC 2019
A hybrid CNN-Transformer framework called LHCT-Net reached 98.55% accuracy for melanoma-versus-non-melanoma classification on the 25,331-image ISIC 2019 dataset.
A hybrid CNN-Transformer framework called LHCT-Net reached 98.55% accuracy for melanoma-versus-non-melanoma classification on the 25,331-image ISIC 2019 dataset.
An explainable transformer model fusing MRI with texture descriptors reached over 99 percent accuracy in Alzheimer's disease staging benchmarks, with ...
An AI model trained on nearly 6,000 annotated uterine ultrasound images automatically segments the endometrium, measures its thickness with sub-millimeter ...
A meta-analysis of twenty studies found point-of-care lung ultrasound diagnosed childhood pneumonia with higher pooled sensitivity and specificity than chest ...
A selective dual-key encryption scheme uses hybrid chaotic maps to protect critical regions of medical CT images while supporting real-time ...
A new 3D Mamba-based model segments brain tumors with consistent accuracy gains and a 15x efficiency improvement over existing state-of-the-art ...
A systematic review of 26 studies finds AI models diagnosing multiple sclerosis with over 95 percent accuracy in some cases, ...
SalientNet is an unsupervised framework that generates mechanistically grounded saliency maps for CNN medical image classifiers, outperforming Grad-CAM, Grad-CAM++, and ...
A SegFormer-B2-based multi-expert ensemble called MUSE-Net achieved a mean Dice of 0.834 for carotid plaque segmentation across a four-center ultrasound ...
A pilot study reports that an infrared heating and imaging system distinguished cancer-free from cancer-positive surgical margins with 93.33% accuracy ...
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© 2025 Scienmag - Science Magazine