Tag: Grad-CAM

Researchers have built an interpretable 2.5D deep learning model that fuses contrast-enhanced CT features with clinical variables to reliably distinguish ...

A South Korean team has combined U-Net segmentation, hyperbolic mathematical modeling, and Fast R-CNN classification to automate suprahyoid muscle ultrasound ...

Researchers have developed SkinEnsemNet, an ensemble deep learning framework combining VGG16, DenseNet201, and EfficientNetB0 that achieves up to 98.85 percent ...

Researchers in Pakistan trained deep learning models to distinguish four histopathologically similar inflammatory skin diseases with up to 96.67 percent ...

Researchers in South Korea have developed a fully automated deep-learning system that uses supervised contrastive learning across multiphase CT scans ...

Researchers in Ethiopia have built an interpretable deep learning ensemble that identifies malaria parasite species from thin blood smears with ...

A new review of 61 studies reveals that the explainability and privacy protections required for trustworthy medical and biometric deep ...

Researchers have developed MFKR Net, a lightweight deep learning model that identifies individuals from contactless middle finger knuckle images with ...

Researchers in Bangladesh have developed an explainable deep learning framework that classifies brain tumors on MRI scans in 2.45 milliseconds ...

Researchers have unveiled a weakly supervised AI framework that segments crops, weeds and soil from image-level labels alone, achieving 76.04 ...