Classification and Visualisation of Normal and Abnormal Radiographs; A Comparison between Eleven Convolutional Neural Network Architectures

This paper investigates the classification of radiographic images with eleven convolutional neural network (CNN) architectures (GoogleNet, VGG-19, AlexNet, SqueezeNet, ResNet-18, Inception-v3, ResNet-50, VGG-16, ResNet-101, DenseNet-201 and Inception-ResNet-v2). The CNNs were used to classify a seri...

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Detalles Bibliográficos
Autores principales: Ananda, Ananda, Ngan, Kwun Ho, Karabağ, Cefa, Ter-Sarkisov, Aram, Alonso, Eduardo, Reyes-Aldasoro, Constantino Carlos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8400172/
https://www.ncbi.nlm.nih.gov/pubmed/34450821
http://dx.doi.org/10.3390/s21165381

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8400172/
https://www.ncbi.nlm.nih.gov/pubmed/34450821
http://dx.doi.org/10.3390/s21165381

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