Classification of white blood cells using capsule networks

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dc.authoridATILA, UMIT/0000-0002-1576-9977
dc.authoridBaydilli, Yusuf Yargi/0000-0002-4457-2081
dc.contributor.authorBaydilli, Yusuf Yargi
dc.contributor.authorAtila, Umit
dc.date.accessioned2024-09-29T15:55:14Z
dc.date.available2024-09-29T15:55:14Z
dc.date.issued2020
dc.departmentKarabük Üniversitesien_US
dc.description.abstractBackground: While the number and structural features of white blood cells (WBC) can provide important information about the health status of human beings, the ratio of sub-types of these cells and the deformations that can be observed serve as a good indicator in the diagnosis process of some diseases. Hence, correct identification and classification of the WBC types is of great importance. In addition, the fact that the diagnostic process that is carried out manually is slow, and the success is directly proportional to the expert's skills makes this problem an excellent field of application for computer-aided diagnostic systems. Unfortunately, both the ethical reasons and the cost of image acquisition process is one of the biggest obstacles to the fact that researchers working with medical images are able to collect enough data to produce a stable model. For that reasons, researchers who want to perform a successful analysis with small data sets using classical machine learning methods need to undergo their data a long and error-prone pre-process, while those using deep learning methods need to increase the data size using augmentation techniques. As a result, there is a need for a model that does not need pre-processing and can perform a successful classification in small data sets. Methods: WBCs were classified under five categories using a small data set via capsule networks, a new deep learning method. We improved the model using many techniques and compared the results with the most known deep learning methods. Results: Both the above-mentioned problems were overcame and higher success rates were obtained compared to other deep learning models. While, convolutional neural networks (CNN) and transfer learning (TL) models suffered from over-fitting, capsule networks learned well training data and achieved a high accuracy on test data (96.86%). Conclusion: In this study, we briefly discussed the abilities of capsule networks in a case study. We showed that capsule networks are a quite successful alternative for deep learning and medical data analysis when the sample size is limited. (C) 2020 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.compmedimag.2020.101699
dc.identifier.issn0895-6111
dc.identifier.issn1879-0771
dc.identifier.pmid32000087en_US
dc.identifier.scopus2-s2.0-85078219376en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.compmedimag.2020.101699
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4520
dc.identifier.volume80en_US
dc.identifier.wosWOS:000517850500006en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputerized Medical Imaging and Graphicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMedical image analysisen_US
dc.subjectWhite blood cells (WBC)en_US
dc.subjectDeep learningen_US
dc.subjectCapsule networksen_US
dc.subjectClassificationen_US
dc.titleClassification of white blood cells using capsule networksen_US
dc.typeArticleen_US

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