Tag: convolutional neural networks

A new study reports three neural modules that cut computational cost by 26.7 percent while improving accuracy benchmarks for real-time ...

SalientNet is an unsupervised framework that generates mechanistically grounded saliency maps for CNN medical image classifiers, outperforming Grad-CAM, Grad-CAM++, and ...

A systematic review of 46 studies finds deep learning promising for post-earthquake structural damage assessment, but weak generalisation evidence and ...

A systematic comparison of nine CNN architectures on label-free multimodal microscopy images of colonic tissue found ResNet50 with partial fine-tuning ...

A systematic review of 94 studies finds that deep learning models, especially GAN-based architectures, outperform classical methods for underwater image ...

A new two-layer hypergraph model simulates opinion change and workplace switching, with LSTM and XGBoost performing best at estimating its ...

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 new review argues that deep learning skin lesion analysis must prove itself through external validation, fairness and clinical utility ...

A systematic review finds deep learning exceeds 97 percent accuracy on remote sensing benchmarks but struggles with generalization, spectral exploitation ...

Pretrained convolutional neural networks classified biological sex from smartphone photographs of palatal rugae with up to 77 percent accuracy in ...

A customized VGG19 deep learning model trained on 2,100 chest X-rays from Ethiopian hospitals classified pneumonia and tuberculosis with 99% ...

A controlled benchmark of five CNN architectures on 2,000 intraoral photographs found EfficientNet-B0 led with 98.18 percent accuracy, though no ...

A systematic review of 59 studies finds deep learning dominates fungal leaf disease detection while bacterial diagnosis, generative AI and ...

A new review finds that AI models reading digital images of culture plates can detect microbial growth with sensitivities of ...

A new federated learning framework called NeuroSono-Net classifies neonatal brain ultrasound conditions across simulated institutions while keeping patient data local, ...

A 240-run reevaluation of potato disease classifiers shows that DINOv2 visual clusters poorly match published leaf identities and that grouped ...

A particle swarm optimization strategy that dynamically selects the most informative clients in federated learning matches state-of-the-art methods for medical ...

A sine-guided Transformer–CNN network significantly outperformed 21 comparison methods on three benchmark datasets for salient-object detection in optical remote-sensing imagery.

A systematic review of 168 primary studies found that 57.14% did not examine the performance–faithfulness relationship, 41.07% reported synergy and ...

A new study in Scientific Reports introduces a hybrid framework using variable fuzzy sets and convolutional neural networks to assess ...