ESC-UNET: A hybrid CNN and Swin Transformers for skin lesion segmentation

Anwar Jimi; Nabila Zrira; Oumaima Guendoul; Ibtissam Benmiloud; Haris Ahmad Khan; Shah Nawaz

doi:10.1016/j.ibmed.2025.100257

ScienceGate Book Chapters

JOURNAL ARTICLE

ESC-UNET: A hybrid CNN and Swin Transformers for skin lesion segmentation

Anwar Jimi Nabila Zrira Oumaima Guendoul Ibtissam Benmiloud Haris Ahmad Khan Shah Nawaz

Year: 2025 Journal: Intelligence-Based Medicine Vol: 12 Pages: 100257-100257 Publisher: Elsevier BV

DOI: 10.1016/j.ibmed.2025.100257

Get Full-Text PDF Get Analytical Report

Keywords:

Segmentation Lesion Artificial intelligence Transformer Computer science Computer vision Medicine Engineering Pathology Electrical engineering Voltage

Metrics

Cited By

6.54

FWCI (Field Weighted Citation Impact)

Refs

0.91

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Cutaneous Melanoma Detection and Management

Health Sciences → Medicine → Oncology

AI in cancer detection

Physical Sciences → Computer Science → Artificial Intelligence

Digital Imaging for Blood Diseases

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

ESC-UNET: A hybrid CNN and Swin Transformers for skin lesion segmentation

Metrics

Citation History

Topics

Related Documents

Skin Lesion Segmentation with C-UNet

Hybrid channel and spatial attention-UNet for skin lesion segmentation

Robust Automated Mouse Micro-CT Segmentation Using Swin UNEt TRansformers

DENSE SWIN-UNET: DENSE SWIN TRANSFORMERS FOR SEMANTIC SEGMENTATION OF PNEUMOTHORAX IN CT IMAGES

HAUS: A Hybrid Attention-UNet framework for enhanced skin lesion Segmentation