Results 141 to 150 of about 8,876 (216)

Dual-SwinOrd: A Dual-Head Swin Transformer with Semantic Prior Injection for Ordinal Diabetic Retinopathy Grading. [PDF]

Bioengineering (Basel)
Yu W, Si X, Zhong J.
europepmc   +1 more source

YOLOv13-SwinTongue: Tongue Coating Diagnosis Using an Enhanced YOLOv13 with Swin Transformer. [PDF]

Sensors (Basel)
Yang X, Hao J, Wang Y, Man Y, Yang R, Wu Q.   +5 more
europepmc   +1 more source

Retraction Note: Plant disease recognition using residual convolutional enlightened Swin transformer networks. [PDF]

Sci Rep
Kalpana P, Anandan R, Hussien AG, Migdady H, Abualigah L.   +4 more
europepmc   +1 more source

HASwinNet: A Swin Transformer-Based Denoising Framework with Hybrid Attention for mmWave MIMO Systems. [PDF]

Entropy (Basel)
Han X, Tu H, Ying J, Chen J, Xing Z.
europepmc   +1 more source

AI-driven multimodal imaging fusion using swin transformer and optimized tensor fusion networks for pneumonia detection. [PDF]

Sci Rep
Sikindar S, Raghavendran CV, Madhavi G.
europepmc   +1 more source

A deep learning framework for breast cancer diagnosis using Swin Transformer and Dual-Attention Multi-scale Fusion Network. [PDF]

Sci Rep
Aldawsari MA, Aldosari SJ, Ismail A, Emam MM.   +3 more
europepmc   +1 more source

BC-SwinNet: Swin transformer and CNN with multi-objective optimization for multi-class breast cancer detection using Histopathological images. [PDF]

Sci Rep
Khan M, Altaf M, Khan NR, Pk H, Malik V, Menshawi A, Alsubait T, Ziar RA, Martin RJ.   +8 more
europepmc   +1 more source

Optimized federated learning framework with RegNetZ and Swin-Transformer for multimodal pancreatic cancer detection1. [PDF]

Sci Rep
Ge W, Govindarajan V, Yang J, Ayadi M, Shaikh ZA, Li L, Por LY, Liu N, Tu Y.   +8 more
europepmc   +1 more source

Mapping Historical Landslide Activity Using a Swin Transformer-Based Transfer Learning Approach. [PDF]

Sensors (Basel)
Chen F, Liang Z, Cheng Z, Li H, Zhong C, Hu Z.   +5 more
europepmc   +1 more source

POST: photonic swin transformer for automated and efficient prediction of PCSEL. [PDF]

Nanophotonics
Xin Q, Huang H, Li C, Shi K, Zhang Z.
europepmc   +1 more source