dc.contributor | Janković, Predrag | |
dc.creator | Laban, Lara | |
dc.creator | Vesović, Mitra | |
dc.date.accessioned | 2023-02-23T12:08:43Z | |
dc.date.available | 2023-02-23T12:08:43Z | |
dc.date.issued | 2020 | |
dc.identifier.isbn | 978-86-6055-139-1 | |
dc.identifier.uri | https://machinery.mas.bg.ac.rs/handle/123456789/4508 | |
dc.description.abstract | In this paper a method is presented for the
classification of COVID-CT (CT_COVID, CT_NonCOVID)
image data set. Four different types of deep convolutional
neural networks are proposed, two with the architecture
resembling the VGGNet, one resembling the LeNet-5 and
one using transfer learning. In addition, neural networks
utilized the following techniques: decay, dropout and batch
normalization. Since we needed to combat a significantly
small dataset, we used data augmentation in order to
transform and expand our dataset. Moreover,
juxtapositions were made when observing the results given
by these four neural networks, as well as the affect made by
two different optimizers. The training of the neural
networks was done using small batches with a binary cross
entropy loss function, in order to achieve an up to scratch
classification accuracy. | sr |
dc.language.iso | en | sr |
dc.publisher | Faculty of Mechanical Engineering in Niš | sr |
dc.publisher | Prof. Dr Nenad T. Pavlović, Dean | sr |
dc.relation | info:eu-repo/grantAgreement/ScienceFundRS/AI/6523109/RS// | sr |
dc.relation | info:eu-repo/grantAgreement/MESTD/inst-2020/200105/RS// | sr |
dc.relation | info:eu-repo/grantAgreement/MESTD/Technological Development (TD or TR)/35029/RS// | sr |
dc.rights | openAccess | sr |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.source | THE FIFTH INTERNATIONAL CONFERENCE “MECHANICAL ENGINEERING IN XXI CENTURY” - MASING 2020 PROCEEDINGS -UNIVERSITY OF NIŠ FACULTY OF MECHANICAL ENGINEERING IN NIŠ, December 9-10. | sr |
dc.subject | deep learning | sr |
dc.subject | convolutional neural networks | sr |
dc.subject | image classification | sr |
dc.subject | data augmentation | sr |
dc.subject | batch normalization | sr |
dc.subject | COVID-CT dataset | sr |
dc.subject | dropout | sr |
dc.subject | transfer learning | sr |
dc.title | Classification of COVID-CT Images Utilizing Four Types of Deep Convolutional Neural Networks | sr |
dc.type | conferenceObject | sr |
dc.rights.license | BY | sr |
dc.citation.epage | 206 | |
dc.citation.rank | M33 | |
dc.citation.spage | MECHATRONICS AND CONTROL pp. 201 | |
dc.identifier.fulltext | http://machinery.mas.bg.ac.rs/bitstream/id/10776/MASING_2020_Proceedings_212.pdf | |
dc.identifier.rcub | https://hdl.handle.net/21.15107/rcub_machinery_4508 | |
dc.type.version | publishedVersion | sr |