Self-supervised deep learning spots cotton leaf diseases with explainable heatmaps
A self-supervised CNN framework with Grad-CAM heatmaps reached up to 94.38% validation accuracy on a 2,137-image cotton leaf disease dataset.
A self-supervised CNN framework with Grad-CAM heatmaps reached up to 94.38% validation accuracy on a 2,137-image cotton leaf disease dataset.
A dual-decoder deep-learning network localized 14 shoulder bony landmarks on contralateral CT scans with a mean radial error of 2.1 ...
A learning-based hybrid optimization method reduced median normalized IGD+ by 60.6% versus standard NSGA-II in a modeled circular e-waste supply ...
A retrospective single-center study of 447 patients found that a model integrating glypican-3 with clinical features predicted overall and time-specific ...
A laboratory study using machine learning to optimize hydrothermal production of carbon quantum dots from tea processing waste reported a ...
A model-development study using 471 specialist nurse samples achieved an AUC of 0.766 for distinguishing stated transfer intentions, while highlighting ...
A computational modeling study found that lower fractional orders increased disease-free equilibrium stability and that higher vaccination coverage and treatment ...
A deep neural network lost the accuracy race against random forest and SVM on a genetic disease benchmark, but its ...
Excavators are among the most recognizable machines on Earth, their articulated arms swinging over construction sites, open-pit mines, and disaster ...
A deep learning model that fuses PET imaging features with clinical data predicted breast cancer molecular subtypes non-invasively, reaching an ...
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© 2025 Scienmag - Science Magazine