Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification
Saved in:
| Main Authors: | Saleem Mustafa, Arfan Jaffar, Muhammad Rashid, Sheeraz Akram, Sohail Masood Bhatti |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11737724/?tool=EBI |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification.
by: Saleem Mustafa, et al.
Published: (2025-01-01) -
LA-ResUNet: An Efficient Linear Attention Mechanism in ResUNet for the Semantic Segmentation of Pulmonary Nodules
by: P. C. Sarah Prithvika, et al.
Published: (2024-01-01) -
A Multiattention ResUNet and Modified U-Net Architecture for Liver Tumor Segmentation
by: Justice Kwame Appati, et al.
Published: (2024-01-01) -
Fault analysis on deep groove ball bearing using ResNet50 and AlexNet50 algorithms
by: Vedant Jaiswal, et al.
Published: (2025-04-01) -
Application of ResUNet-CBAM in Thin-Section Image Segmentation of Rocks
by: Ling Zhao, et al.
Published: (2024-12-01)