Results 71 to 80 of about 7,104 (161)

A novel framework for rosacea detection using Swin Transformers and explainable artificial intelligence

Alexandria Engineering Journal
Accurate and efficient diagnosis of skin rosacea is crucial in dermatological healthcare, yet remains challenging due to the need for precise classification and interpretability.
Anjali T, S. Abhishek, Remya S
doaj   +1 more source

A self attention based deep learning framework for accurate and efficient dental disease detection in OPG radiographs

Scientific Reports
Oral diseases are increasing now-a-days and there is a high demand for the automatic diagnostic system that helps the clinician to detect these oral diseases with more accuracy and reduced human error.
Ramasubramanian Bhoopalan, S. Mirdula, Priyadharshini Kannusamy, D. Gayathri, D. Manikandan, Krishnaraj Ramaswamy   +5 more
doaj   +1 more source

Off-line identifying Script Writers by Swin Transformers and ResNeSt-50

ELCVIA Electronic Letters on Computer Vision and Image Analysis
In this work, we present two advanced models for identifying script writers, leveraging the power of deep learning. The proposed systems utilize the new vision Swin Transformer and ResNeSt-50.
Afef Kacem Echi, Takwa Ben Aïcha Gader
doaj   +1 more source

Vision‐Language Models for Automated Chest X‐ray Interpretation: Leveraging ViT and GPT‐2

Engineering Reports
Radiology plays a pivotal role in modern medicine due to its non‐invasive diagnostic capabilities. However, the manual generation of unstructured medical reports is time‐consuming and prone to errors.
Md. Rakibul Islam, Md. Zahid Hossain, Mustofa Ahmed, Most. Sharmin Sultana Samu   +3 more
doaj   +1 more source

A New Subject-Sensitive Hashing Algorithm Based on Multi-PatchDrop and Swin-Unet for the Integrity Authentication of HRRS Image

ISPRS International Journal of Geo-Information
Transformer-based subject-sensitive hashing algorithms exhibit good integrity authentication performance and have the potential to ensure the authenticity and convenience of high-resolution remote sensing (HRRS) images.
Kaimeng Ding, Yingying Wang, Chishe Wang, Ji Ma   +3 more
doaj   +1 more source

Transfer Learning With U-Swin Transformer for Adaptive Ground Roll Attenuation in Seismic Records From Sabkha Environments

IEEE Access
Ground roll is a dominant coherent noise in land seismic data, characterized by low frequency, low velocity, and high amplitude. often overlaps with reflection arrivals, thereby reducing the reliability of seismic imaging and interpretation.
Ahmed Eleslambouly, Guibin Zhao, Fateh Bouchaala, Mohammed Y. Ali, Jun Matsushima   +4 more
doaj   +1 more source

EA-Swin: An Embedding-Agnostic Swin Transformer for AI-Generated Video Detection

CoRR
Recent advances in foundation video generators such as Sora2, Veo3, and other commercial systems have produced highly realistic synthetic videos, exposing the limitations of existing detection methods that rely on shallow embedding trajectories, image-based adaptation, or computationally heavy MLLMs.
Hung Mai, Loi Dinh, Duc Hai Nguyen, Dat Do, Luong Doan, Khanh Nguyen Quoc, Huan Vu, Phong Ho, Naeem Ul Islam, Tuan Do   +9 more
openaire   +2 more sources

Swin Routiformer: Moss Classification Algorithm Based on Swin Transformer With Bi-Level Routing Attention

IEEE Access
Accurate classification of moss species is essential for progress in ecology and biology. However, traditional methods for classifying moss require significant expertise, and current deep learning techniques struggle due to limited dataset diversity and ...
Peichen Li, Huiqin Wang, Zhan Wang, Ke Wang, Chong Wang   +4 more
doaj   +1 more source

SE‐Swin: An improved Swin‐Transfomer network of self‐ensemble feature extraction framework for image retrieval

IET Image Processing
The Swin‐Transformer is a variant of the Vision Transformer, which constructs a hierarchical Transformer that computes representations with shifted windows and window multi‐head self‐attention.
Yixuan Xu, Xianbing Wang, Hua Zhang, Hai Lin   +3 more
doaj   +1 more source

Transformers meet CNNs for insights into breast mass classification from histopathological images

Frontiers in Artificial Intelligence
IntroductionBreast cancer remains one of the leading causes of cancer-related deaths among women worldwide, highlighting the critical need for accurate histopathological diagnosis and reliable decision-support systems to improve diagnostic sensitivity ...
Vatsala Anand, Ajay Khajuria
doaj   +1 more source