Deep convolutional neural networks for automated scoring of pentagon copying test results

This study aims to investigate the accuracy of a fine-tuned deep convolutional neural network (CNN) for evaluating responses to the pentagon copying test (PCT). To develop a CNN that could classify PCT images, we fine-tuned and compared the pre-trained CNNs (GoogLeNet, VGG-16, ResNet-50, Inception-v...

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Detalles Bibliográficos
Autores principales: Maruta, Jumpei, Uchida, Kentaro, Kurozumi, Hideo, Nogi, Satoshi, Akada, Satoshi, Nakanishi, Aki, Shinoda, Miki, Shiba, Masatsugu, Inoue, Koki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9198090/
https://www.ncbi.nlm.nih.gov/pubmed/35701481
http://dx.doi.org/10.1038/s41598-022-13984-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9198090/
https://www.ncbi.nlm.nih.gov/pubmed/35701481
http://dx.doi.org/10.1038/s41598-022-13984-7

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