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A novel architecture to classify histopathology images using convolutional neural networks
ID
Kandel, Ibrahem
(
Avtor
),
ID
Castelli, Mauro
(
Avtor
)
PDF - Predstavitvena datoteka,
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(4,19 MB)
MD5: 1EABD12E65615320172EB58E88F1A1D9
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Izvleček
Histopathology is the study of tissue structure under the microscope to determine if the cells are normal or abnormal. Histopathology is a very important exam that is used to determine the patients’ treatment plan. The classification of histopathology images is very difficult to even an experienced pathologist, and a second opinion is often needed. Convolutional neural network (CNN), a particular type of deep learning architecture, obtained outstanding results in computer vision tasks like image classification. In this paper, we propose a novel CNN architecture to classify histopathology images. The proposed model consists of 15 convolution layers and two fully connected layers. A comparison between different activation functions was performed to detect the most efficient one, taking into account two different optimizers. To train and evaluate the proposed model, the publicly available PatchCamelyon dataset was used. The dataset consists of 220,000 annotated images for training and 57,000 unannotated images for testing. The proposed model achieved higher performance compared to the state-of-the-art architectures with an AUC of 95.46%.
Jezik:
Angleški jezik
Vrsta gradiva:
Članek v reviji
Tipologija:
1.01 - Izvirni znanstveni članek
Organizacija:
EF - Ekonomska fakulteta
Različica publikacije:
Objavljena publikacija
Leto izida:
2020
Št. strani:
Str. 1-17
Številčenje:
Vol. 10, iss. 8 (art. 2929)
PID:
20.500.12556/RUL-124233
UDK:
004:78
ISSN pri članku:
2076-3417
DOI:
10.3390/app10082929
COBISS.SI-ID:
38419459
Datum objave v RUL:
11.01.2021
Število ogledov:
1022
Število prenosov:
205
Metapodatki:
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Objavi na:
Gradivo je del revije
Naslov:
Applied sciences
Skrajšan naslov:
Appl. sci.
Založnik:
MDPI
ISSN:
2076-3417
COBISS.SI-ID:
522979353
Licence
Licenca:
CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:
http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:
To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
Začetek licenciranja:
11.01.2021
Projekti
Financer:
ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Številka projekta:
P5-0410
Naslov:
Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe
Financer:
FCT - Fundação para a Ciência e a Tecnologia, I.P.
Številka projekta:
DSAIPA/DS/0022/2018
Naslov:
GADgET
Financer:
FCT - Fundação para a Ciência e a Tecnologia, I.P.
Številka projekta:
DSAIPA/DS/0113/2019
Naslov:
AICE
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