izpis_h1_title_alt

Improving convolutional neural networks performance for image classification using test time augmentation : a case study using MURA dataset
ID Kandel, Ibrahem (Avtor), ID Castelli, Mauro (Avtor)

.pdfPDF - Predstavitvena datoteka, prenos (1,82 MB)
MD5: 58E488E1D8AB9FA76521D568236D9BD4
URLURL - Izvorni URL, za dostop obiščite https://link.springer.com/article/10.1007%2Fs13755-021-00163-7 Povezava se odpre v novem oknu

Izvleček
Bone fractures are one of the main causes to visit the emergency room (ER); the primary method to detect bone fractures is using X-Ray images. X-Ray images require an experienced radiologist to classify them; however, an experienced radiologist is not always available in the ER. An accurate automatic X-Ray image classifier in the ER can help reduce error rates by providing an instant second opinion to the emergency doctor. Deep learning is an emerging trend in artificial intelligence, where an automatic classifier can be trained to classify musculoskeletal images. Image augmentations techniques have proven their usefulness in increasing the deep learning model's performance. Usually, in the image classification domain, the augmentation techniques are used during training the network and not during the testing phase. Test time augmentation (TTA) can increase the model prediction by providing, with a negligible computational cost, several transformations for the same image. In this paper, we investigated the effect of TTA on image classification performance on the MURA dataset. Nine different augmentation techniques were evaluated to determine their performance compared to predictions without TTA. Two ensemble techniques were assessed as well, the majority vote and the average vote. Based on our results, TTA increased classification performance significantly, especially for models with a low score.

Jezik:Angleški jezik
Ključne besede:image classification, convolutional neural networks, transfer learning, test time 29 augmentation, deep learning, ensemble learning
Vrsta gradiva:Članek v reviji
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:EF - Ekonomska fakulteta
Status publikacije:Objavljeno
Različica publikacije:Recenzirani rokopis
Leto izida:2021
Št. strani:22 str.
Številčenje:Vol. 9, art 33
PID:20.500.12556/RUL-129209 Povezava se odpre v novem oknu
UDK:659.2:004
ISSN pri članku:2047-2501
DOI:10.1007/s13755-021-00163-7 Povezava se odpre v novem oknu
COBISS.SI-ID:72230659 Povezava se odpre v novem oknu
Datum objave v RUL:30.08.2021
Število ogledov:726
Število prenosov:173
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
:
Kopiraj citat
Objavi na:Bookmark and Share

Gradivo je del revije

Naslov:Health information science and systems
Skrajšan naslov:Health inf. sci. syst.
Založnik:Springer
ISSN:2047-2501
COBISS.SI-ID:523110681 Povezava se odpre v novem oknu

Projekti

Financer:FCT - Fundação para a Ciência e a Tecnologia, I.P.
Številka projekta:DSAIPA/DS/0022/2018
Naslov:GADgET

Financer:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Program financ.:Raziskovalni program
Številka projekta:P5-0410
Naslov:Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe

Podobna dela

Podobna dela v RUL:
Podobna dela v drugih slovenskih zbirkah:

Nazaj