Cargando…
Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine
The variation in skin textures and injuries, as well as the detection and classification of skin cancer, is a difficult task. Manually detecting skin lesions from dermoscopy images is a difficult and time-consuming process. Recent advancements in the domains of the internet of things (IoT) and artif...
Autores principales: | Afza, Farhat, Sharif, Muhammad, Khan, Muhammad Attique, Tariq, Usman, Yong, Hwan-Seung, Cha, Jaehyuk |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838278/ https://www.ncbi.nlm.nih.gov/pubmed/35161553 http://dx.doi.org/10.3390/s22030799 |
Ejemplares similares
-
A Computer-Aided Diagnosis System Using Deep Learning for Multiclass Skin Lesion Classification
por: Arshad, Mehak, et al.
Publicado: (2021) -
Skin Lesion Segmentation and Multiclass Classification Using Deep Learning Features and Improved Moth Flame Optimization
por: Khan, Muhammad Attique, et al.
Publicado: (2021) -
A novel framework of multiclass skin lesion recognition from dermoscopic images using deep learning and explainable AI
por: Ahmad, Naveed, et al.
Publicado: (2023) -
SkinNet-INIO: Multiclass Skin Lesion Localization and Classification Using Fusion-Assisted Deep Neural Networks and Improved Nature-Inspired Optimization Algorithm
por: Hussain, Muneezah, et al.
Publicado: (2023) -
An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization
por: Nawaz, Marriam, et al.
Publicado: (2022)