A study on skin tumor classification based on dense convolutional networks with fused metadata
Skin cancer is the most common cause of death in humans. Statistics show that competent dermatologists have a diagnostic accuracy rate of less than 80%, while inexperienced dermatologists have a diagnostic accuracy rate of less than 60%. The higher rate of misdiagnosis will cause many patients to mi...
Main Authors: | Wenjun Yin, Jianhua Huang, Jianlin Chen, Yuanfa Ji |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2022-12-01
|
Series: | Frontiers in Oncology |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.989894/full |
Similar Items
-
Rethinking Densely Connected Convolutional Networks for Diagnosing Infectious Diseases
by: Prajoy Podder, et al.
Published: (2023-05-01) -
Nutrients deficiency diagnosis of rice crop by weighted average ensemble learning
by: Md. Simul Hasan Talukder, et al.
Published: (2023-08-01) -
Alzheimer’s disease diagnosis and classification using deep learning techniques
by: Waleed Al Shehri
Published: (2022-12-01) -
An Adaptive Early Stopping Technique for DenseNet169-Based Knee Osteoarthritis Detection Model
by: Bander Ali Saleh Al-rimy, et al.
Published: (2023-05-01) -
Garbage Classification Using Ensemble DenseNet169
by: Ulfah Nur Oktaviana, et al.
Published: (2021-12-01)