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Automatic detection of white blood cancer from bone marrow microscopic images using convolutional neural networks

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dc.title Automatic detection of white blood cancer from bone marrow microscopic images using convolutional neural networks en
dc.contributor.author Kumar, Deepika
dc.contributor.author Jain, Nikita
dc.contributor.author Khurana, Aayush
dc.contributor.author Mittal, Sweta
dc.contributor.author Satapathy, Suresh Chandra
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Hemanth, Jude D.
dc.relation.ispartof IEEE Access
dc.identifier.issn 2169-3536 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2020
utb.relation.volume 8
dc.citation.spage 142521
dc.citation.epage 142531
dc.type article
dc.language.iso en
dc.publisher Institute of Electrical and Electronics Engineers Inc.
dc.identifier.doi 10.1109/ACCESS.2020.3012292
dc.relation.uri https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9149873
dc.subject cancer en
dc.subject blood en
dc.subject feature extraction en
dc.subject bones en
dc.subject convolutional neural networks en
dc.subject cells (biology) en
dc.subject machine learning en
dc.subject acute lymphoblastic leukemia en
dc.subject classification algorithms en
dc.subject deep learning en
dc.subject convolutional neural networks en
dc.subject image processing en
dc.subject multiple myeloma en
dc.description.abstract Leukocytes, produced in the bone marrow, make up around one percent of all blood cells. Uncontrolled growth of these white blood cells leads to the birth of blood cancer. Out of the three different types of cancers, the proposed study provides a robust mechanism for the classification of Acute Lymphoblastic Leukemia (ALL) and Multiple Myeloma (MM) using the SN-AM dataset. Acute lymphoblastic leukemia (ALL) is a type of cancer where the bone marrow forms too many lymphocytes. On the other hand, Multiple myeloma (MM), a different kind of cancer, causes cancer cells to accumulate in the bone marrow rather than releasing them into the bloodstream. Therefore, they crowd out and prevent the production of healthy blood cells. Conventionally, the process was carried out manually by a skilled professional in a considerable amount of time. The proposed model eradicates the probability of errors in the manual process by employing deep learning techniques, namely convolutional neural networks. The model, trained on cells' images, first pre-processes the images and extracts the best features. This is followed by training the model with the optimized Dense Convolutional neural network framework (termed DCNN here) and finally predicting the type of cancer present in the cells. The model was able to reproduce all the measurements correctly while it recollected the samples exactly 94 times out of 100. The overall accuracy was recorded to be 97.2%, which is better than the conventional machine learning methods like Support Vector Machine (SVMs), Decision Trees, Random Forests, Naive Bayes, etc. This study indicates that the DCNN model's performance is close to that of the established CNN architectures with far fewer parameters and computation time tested on the retrieved dataset. Thus, the model can be used effectively as a tool for determining the type of cancer in the bone marrow. © 2013 IEEE. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1009889
utb.identifier.obdid 43881326
utb.identifier.scopus 2-s2.0-85089944328
utb.identifier.wok 000560639000001
utb.source j-scopus
dc.date.accessioned 2020-09-15T13:41:18Z
dc.date.available 2020-09-15T13:41:18Z
dc.rights Attribution 4.0 International
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.rights.access openAccess
utb.contributor.internalauthor Šenkeřík, Roman
utb.fulltext.affiliation DEEPIKA KUMAR 1, NIKITA JAIN 1, AAYUSH KHURANA 1 (Student Member, IEEE), SWETA MITTAL 1, SURESH CHANDRA SATAPATHY 2 (Senior Member, IEEE), ROMAN SENKERIK 3 (Member, IEEE), JUDE D. HEMANTH 4 1 Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi 110063, India 2 School of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to be University), Bhubaneswar 751024, India 3 Faculty of Applied Informatics, Tomas Bata University in Zlin, 76001 Zlin, Czech Republic 4 Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore 641114, India Corresponding authors: Jude D. Hemanth (judehemanth@karunya.edu) and Roman Senkerik (senkerik@utb.cz)
utb.fulltext.dates Received July 9, 2020 accepted July 15, 2020 date of publication July 27, 2020 date of current version August 14, 2020
utb.fulltext.sponsorship This work was supported by the Resources of A.I.Lab, Faculty of Applied Informatics, Tomas Bata University in Zlin.
utb.wos.affiliation [Kumar, Deepika; Jain, Nikita; Khurana, Aayush; Mittal, Sweta] Bharati Vidyapeeths Coll Engn, Dept Comp Sci & Engn, New Delhi 110063, India; [Satapathy, Suresh Chandra] Deemed Be Univ, Kalinga Inst Ind Technol, Sch Comp Engn, Bhubaneswar 751024, India; [Senkerik, Roman] Tomas Bata Univ Zlin, Fac Appl Informat, Zlin 76001, Czech Republic; [Hemanth, Jude D.] Karunya Inst Technol & Sci, Dept ECE, Coimbatore 641114, Tamil Nadu, India
utb.scopus.affiliation Department of Computer Science and Engineering, Bharati Vidyapeeth's College of Engineering, New Delhi, 110063, India; School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, 751024, India; Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, 76001, Czech Republic; Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, 641114, India
utb.fulltext.faculty Faculty of Applied Informatics
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