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Automatic White Blood Cell Detection and Identification Using Convolutional Neural Network


Novoselnik, Filip; Grbic, Ratko; Galic, Irena; Doric, Filip
Automatic White Blood Cell Detection and Identification Using Convolutional Neural Network // Proceedings of the International Conference on Smart Systems and Technologies 2018 (SST 2018)
Osijek: IEEE, 2018. str. 163-167 doi:10.1109/sst.2018.8564625 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


Naslov
Automatic White Blood Cell Detection and Identification Using Convolutional Neural Network

Autori
Novoselnik, Filip ; Grbic, Ratko ; Galic, Irena ; Doric, Filip

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the International Conference on Smart Systems and Technologies 2018 (SST 2018) / - Osijek : IEEE, 2018, 163-167

ISBN
978-1-5386-7189-4

Skup
2018 International Conference on Smart Systems and Technologies (SST)

Mjesto i datum
Osijek, 10-12 Oct. 2018

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
white blood cells ; image segmentation ; convolutional neural networks ; classification ; differential blood count

Sažetak
Differential blood count is a very common medical test which determines relative percentage of each type of white blood cell (WBC) in a blood sample. This test is usually performed by visual inspection of a blood sample which is time consuming and tedious task for a medical specialist. This test can be performed automatically with appropriate equipment as well. However, such equipment is quite expensive and available only at larger medical centers. In this paper alternative approach is proposed which is based on low cost microscope and digital camera coupled with appropriate algorithm for WBCs detection and identification in a blood image. The proposed algorithm consists of two steps. An image of blood sample is segmented in order to detect possible WBCs which are then further classified with Convolutional Neural Network (CNN) into 5 classes. The proposed approach shows promising results obtaining accuracy of 81.11% on created dataset.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA


Ustanove
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek

Profili:

Avatar Url Irena Galić (autor)

Avatar Url Ratko Grbić (autor)

Avatar Url Filip Novoselnik (autor)

Citiraj ovu publikaciju

Novoselnik, Filip; Grbic, Ratko; Galic, Irena; Doric, Filip
Automatic White Blood Cell Detection and Identification Using Convolutional Neural Network // Proceedings of the International Conference on Smart Systems and Technologies 2018 (SST 2018)
Osijek: IEEE, 2018. str. 163-167 doi:10.1109/sst.2018.8564625 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Novoselnik, F., Grbic, R., Galic, I. & Doric, F. (2018) Automatic White Blood Cell Detection and Identification Using Convolutional Neural Network. U: Proceedings of the International Conference on Smart Systems and Technologies 2018 (SST 2018) doi:10.1109/sst.2018.8564625.
@article{article, year = {2018}, pages = {163-167}, DOI = {10.1109/sst.2018.8564625}, keywords = {white blood cells, image segmentation, convolutional neural networks, classification, differential blood count}, doi = {10.1109/sst.2018.8564625}, isbn = {978-1-5386-7189-4}, title = {Automatic White Blood Cell Detection and Identification Using Convolutional Neural Network}, keyword = {white blood cells, image segmentation, convolutional neural networks, classification, differential blood count}, publisher = {IEEE}, publisherplace = {Osijek} }

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