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dc.contributor.authorMuthumayil, K.spa
dc.contributor.authorManikandan, S.spa
dc.contributor.authorSrinivasan, S.spa
dc.contributor.authorEscorcia-Gutierrez, Josespa
dc.contributor.authorGamarra, Margaritaspa
dc.contributor.authorMansour, Romany F.spa
dc.date.accessioned2021-08-23T13:30:02Z
dc.date.available2021-08-23T13:30:02Z
dc.date.issued2021
dc.identifier.issn1546-2218spa
dc.identifier.issn1546-2226spa
dc.identifier.urihttps://hdl.handle.net/11323/8572spa
dc.description.abstractWhite Blood Cell (WBC) cancer or leukemia is one of the serious cancers that threaten the existence of human beings. In spite of its prevalence and serious consequences, it is mostly diagnosed through manual practices. The risks of inappropriate, sub-standard and wrong or biased diagnosis are high in manual methods. So, there is a need exists for automatic diagnosis and classification method that can replace the manual process. Leukemia is mainly classified into acute and chronic types. The current research work proposed a computer-based application to classify the disease. In the feature extraction stage, we use excellent physical properties to improve the diagnostic system’s accuracy, based on Enhanced Color Co-Occurrence Matrix. The study is aimed at identification and classification of chronic lymphocytic leukemia using microscopic images of WBCs based on Enhanced Virtual Neural Network (EVNN) classification. The proposed method achieved optimum accuracy in detection and classification of leukemia from WBC images. Thus, the study results establish the superiority of the proposed method in automated diagnosis of leukemia. The values achieved by the proposed method in terms of sensitivity, specificity, accuracy, and error rate were 97.8%, 89.9%, 76.6%, and 2.2%, respectively. Furthermore, the system could predict the disease in prior through images, and the probabilities of disease detection are also highly optimistic.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoeng
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universalspa
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/spa
dc.sourceComputers, Materials & Continuaspa
dc.subjectWhite blood cellsspa
dc.subjectEnhanced virtual neural networkingspa
dc.subjectSegmentationspa
dc.subjectFeature extractionspa
dc.subjectChronic lymphocytic leukemiaspa
dc.titleDiagnosis of leukemia disease based on enhanced virtual neural networkspa
dc.typeArtículo de revistaspa
dc.source.urlhttps://www.techscience.com/cmc/v69n2/43859spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doidoi:10.32604/cmc.2021.017116spa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.relation.references[1] R. J. A. Cabrera, C. A. P. Legaspi, E. J. G. Papa, R. D. Samonte and D. D. Acula, “Hematic: An automated leukemia detector with separation of overlapping blood cells through image processing and genetic algorithm,” in 2017 Int. Conf. on Applied System Innovation, ICASI 2017, Proceedings: IEEE, Sapporo, Japan, pp. 985–987, 2017.spa
dc.relation.references[2] F. Asadi, F. M. Putra, M. I. Sakinatunnisa, F. Syafria, Okfalisa et al., “Implementation of backpropagation neural network and blood cells imagery extraction for acute leukemia classification,” in 2017 5th Int. Conf. on Instrumentation, Communications, Information Technology, and Biomedical Engineering, Proceedings: IEEE, Bandung, Indonesia, pp. 106–110, 2017.spa
dc.relation.references[3] R. A. M. Budiman, B. Achmad, Faridah, A. Arif, Nopriadi et al., “Localization of white blood cell images using haar cascade classifiers,” in 2016 1st Int. Conf. on Biomedical Engineering, IBIOMED, Proceedings: IEEE, Yogyakarta, Indonesia, pp. 1–5, 2017.spa
dc.relation.references[4] F. Soni, L. Sahu, M. E. Getnet and B. Y. Reta, “Supervised method for acute lymphoblastic leukemia segmentation and classification using image processing,” in 2nd Int. Conf. on Trends in Electronics and Informatics, ICOEI, Tirunelveli, India, pp. 1075–1079, 2018.spa
dc.relation.references[5] Z. Fu, Y. Liu, H. Hu, D. Wu and H. Gao, “An efficient method of white blood cells detection based on artificial bee colony algorithm,” in 2017 29th Chinese Control and Decision Conference, Proceedings: IEEE, Chongqing, China, pp. 3266–3271, 2017.spa
dc.relation.references[6] C. V. Angkoso, I. K. E. Purnama and M. H. Purnomo, “Automatic white blood cell segmentation based on color segmentation and active contour model,” in 2018 Int. Conf. on Intelligent Autonomous Systems, ICOIAS, Proceedings: IEEE, Singapore, pp. 72–76, 2018.spa
dc.relation.references[7] M. Z. Alom, C. Yakopcic, T. M. Taha and V. K. Asari, “Microscopic blood cell classification using inception recurrent residual convolutional neural networks,” in IEEE National Aerospace and Electronics Conference, NAECON 2018, Proceedings: IEEE, Dayton, OH, USA, pp. 222–227, 2018.spa
dc.relation.references[8] F. Yellin, B. D. Haeffele, S. Roth and R. Vidal, “Multi-cell detection and classification using a generative convolutional model,” in IEEE Conf. on Computer Vision and Pattern Recognition, CVPR, Proceedings: IEEE, Salt Lake City, UT, USA, pp. 8953–8961, 2018.spa
dc.relation.references[9] M. Jayasree and N. K. Narayanan, “An efficient mixed noise removal technique from gray scale images using noisy pixel modification technique,” in 2015 Int. Conf. on Communications and Signal Processing, ICCSP, Proceedings: IEEE, Melmaruvathur, India, pp. 336–339, 2015.spa
dc.relation.references[10] Y. Wang, J. Wang, X. Song and L. Han, “An efficient adaptive fuzzy switching weighted mean filter for salt-and-pepper noise removal,” IEEE Signal Processing Letters, vol. 23, no. 11, pp. 205–222, 2016.spa
dc.relation.references[11] G. Moallem, M. Poostchi, H. Yu, K. Silamut, N. Palaniappan et al., “Detecting and segmenting white blood cells in microscopy images of thin blood smears,” in 2017 IEEE Applied Imagery Pattern Recognition Workshop, AIPR, Proceedings: IEEE, Washington, DC, USA, pp. 1–8, 2017.spa
dc.relation.references[12] M. M. Alam and M. T. Islam, “Machine learning approach of automatic identification and counting of blood cells,” Healthcare Technology Letters, vol. 6, no. 4, pp. 103–108, 2019.spa
dc.relation.references[13] M. N. Q. Bhuiyan, S. K. Rahut, R. A. Tanvir and S. Ripon, “Automatic acute lymphoblastic leukemia detection and comparative analysis from images,” in 6th Int. Conf. on Control, Decision and Information Technologies, CODIT, Proceedings: IEEE, Paris, France, pp. 1144–1149, 2019.spa
dc.relation.references[14] H. Li, X. Zhao, A. Su, H. Zhang, J. Liu et al., “Color space transformation and multi-class weighted loss for adhesive white blood cell segmentation,” IEEE Access, vol. 8, pp. 24808–24818, 2020.spa
dc.relation.references[15] N. Dhieb, H. Ghazzai, H. Besbes and Y. Massoud, “An automated blood cells counting and classification framework using mask R-CNN deep learning model,” in 31st Int. Conf. on Microelectronics, ICM, Proceedings: IEEE, Cairo, Egypt, pp. 300–303, 2019.spa
dc.relation.references[16] K. Al-Dulaimi, J. Banks, K. Nguyen, A. Al-Sabaawi, I. T. Reyes et al., “Segmentation of white blood cell, nucleus and cytoplasm in digital haematology microscope images: A review-challenges, current and future potential techniques,” IEEE Reviews in Biomedical Engineering, vol. 14, pp. 1–1, 2020.spa
dc.relation.references[17] Z. Zhong, T. Wang, K. Zeng, X. Zhou and Z. Li, “White blood cell segmentation via sparsity and geometry constraints,” IEEE Access, vol. 7, pp. 167593–167604, 2019.spa
dc.relation.references[18] G. Kolokolnikov and A. Samorodov, “Comparative study of data augmentation strategies for white blood cells classification,” in 25th Conf. of Open Innovations Association, FRUCT, Proceedings: IEEE, Helsinki, Finland, pp. 168–175, 2019.spa
dc.relation.references[19] X. Ma and N. Yu, “Research on low resolution cell image feature fusion algorithm based on convolutional neural network,” in IEEE Int. Conf. on Electron Devices and Solid-State Circuits, EDSSC, Proceedings: IEEE, Xi’an, China, pp. 1–3, 2019.spa
dc.relation.references[20] J. S. Camilleri, L. Farrugia, J. Bonello, N. P. Pace, A. Santorelli et al., “Determining the concentration of red blood cells using dielectric properties,” in 14th European Conf. on Antennas and Propagation, EUCAP, Proceedings: IEEE, Copenhagen, Denmark, pp. 1–5, 2020.spa
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dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa


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