Handling dataset dependence with model ensembles for skin lesion classification from dermoscopic and clinical images-Reference-Cited by-同舟云学术

Handling dataset dependence with model ensembles for skin lesion classification from dermoscopic and clinical images

Published:2022-11-24 Issue:2 Volume:33 Page:556-571
ISSN:0899-9457
Container-title:International Journal of Imaging Systems and Technology
language:en
Short-container-title:Int J Imaging Syst Tech

Author:

Somfai Ellák¹²^ORCID,Baffy Benjámin¹^ORCID,Fenech Kristian¹^ORCID,Hosszú Rita³,Korózs Dorina³^ORCID,Pólik Marcell¹^ORCID,Sárdy Miklós³^ORCID,Lőrincz András¹^ORCID

Affiliation:

1. Department of Artificial Intelligence, Faculty of Informatics Eötvös Loránd University Budapest Hungary

2. Institute for Solid State Physics and Optics Wigner Research Centre for Physics Budapest Hungary

3. Department of Dermatology, Venereology and Dermatooncology, Faculty of Medicine Semmelweis University Budapest Hungary

Funder

European Social Fund

Nemzeti Kutatási Fejlesztési és Innovációs Hivatal

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Software,Electronic, Optical and Magnetic Materials

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ima.22827

Reference47 articles.

1. An ensemble model of convolution and recurrent neural network for skin disease classification

2. Hierarchical Approach for the Classification of Multi-class Skin Lesions Based on Deep Convolutional Neural Networks

3. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

4. Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

5. Increasing the Reliability of Reliability Diagrams

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