izpis_h1_title_alt

The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset
ID Kandel, Ibrahem (Author), ID Castelli, Mauro (Author)

.pdfPDF - Presentation file, Download (578,49 KB)
MD5: FDA0963A73CA0DDBA6BD9414D0C9463F

Abstract
Many hyperparameters have to be tuned to have a robust convolutional neural network that will be able to accurately classify images. One of the most important hyperparameters is the batch size, which is the number of images used to train a single forward and backward pass. In this study, the effect of batch size on the performance of convolutional neural networks and the impact of learning rates will be studied for image classification, specifically for medical images. To train the network faster, a VGG16 network with ImageNet weights was used in this experiment. Our results concluded that a higher batch size does not usually achieve high accuracy, and the learning rate and the optimizer used will have a significant impact as well. Lowering the learning rate and decreasing the batch size will allow the network to train better, especially in the case of fine-tuning.

Language:English
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:EF - School of Economics and Business
Publication version:Version of Record
Year:2020
Number of pages:Str. 312-315
Numbering:Vol. 6, iss. 4
PID:20.500.12556/RUL-124243 This link opens in a new window
UDC:681.5
ISSN on article:2405-9595
DOI:10.1016/j.icte.2020.04.010 This link opens in a new window
COBISS.SI-ID:38422787 This link opens in a new window
Publication date in RUL:12.01.2021
Views:1288
Downloads:404
Metadata:XML RDF-CHPDL DC-XML DC-RDF
:
Copy citation
Share:Bookmark and Share

Record is a part of a journal

Title:ICT express
Publisher:Elsevier
ISSN:2405-9595
COBISS.SI-ID:526132505 This link opens in a new window

Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:12.01.2021

Projects

Funder:ARRS - Slovenian Research Agency
Project number:P5-0410
Name:Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe

Funder:FCT - Fundação para a Ciência e a Tecnologia, I.P.
Project number:DSAIPA/DS/0022/2018
Name:GADgET

Funder:FCT - Fundação para a Ciência e a Tecnologia, I.P.
Project number:DSAIPA/DS/0113/2019
Name:AICE

Similar documents

Similar works from RUL:
Similar works from other Slovenian collections:

Back