Diagnosis of leukemia disease based on enhanced virtual neural network
White 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 i...
- Autores:
-
Muthumayil, K.
Manikandan, S.
Srinivasan, S.
Escorcia-Gutierrez, Jose
Gamarra, Margarita
Mansour, Romany F.
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/8572
- Acceso en línea:
- https://hdl.handle.net/11323/8572
https://repositorio.cuc.edu.co/
- Palabra clave:
- White blood cells
Enhanced virtual neural networking
Segmentation
Feature extraction
Chronic lymphocytic leukemia
- Rights
- openAccess
- License
- CC0 1.0 Universal
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Diagnosis of leukemia disease based on enhanced virtual neural network |
| title |
Diagnosis of leukemia disease based on enhanced virtual neural network |
| spellingShingle |
Diagnosis of leukemia disease based on enhanced virtual neural network White blood cells Enhanced virtual neural networking Segmentation Feature extraction Chronic lymphocytic leukemia |
| title_short |
Diagnosis of leukemia disease based on enhanced virtual neural network |
| title_full |
Diagnosis of leukemia disease based on enhanced virtual neural network |
| title_fullStr |
Diagnosis of leukemia disease based on enhanced virtual neural network |
| title_full_unstemmed |
Diagnosis of leukemia disease based on enhanced virtual neural network |
| title_sort |
Diagnosis of leukemia disease based on enhanced virtual neural network |
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Muthumayil, K. Manikandan, S. Srinivasan, S. Escorcia-Gutierrez, Jose Gamarra, Margarita Mansour, Romany F. |
| dc.contributor.author.spa.fl_str_mv |
Muthumayil, K. Manikandan, S. Srinivasan, S. Escorcia-Gutierrez, Jose Gamarra, Margarita Mansour, Romany F. |
| dc.subject.spa.fl_str_mv |
White blood cells Enhanced virtual neural networking Segmentation Feature extraction Chronic lymphocytic leukemia |
| topic |
White blood cells Enhanced virtual neural networking Segmentation Feature extraction Chronic lymphocytic leukemia |
| description |
White 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. |
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2021 |
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2021-08-23T13:30:02Z |
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2021 |
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https://hdl.handle.net/11323/8572 |
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doi:10.32604/cmc.2021.017116 |
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Corporación Universidad de la Costa |
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eng |
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eng |
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[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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. |
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Muthumayil, K.Manikandan, S.Srinivasan, S.Escorcia-Gutierrez, JoseGamarra, MargaritaMansour, Romany F.2021-08-23T13:30:02Z2021-08-23T13:30:02Z20211546-22181546-2226https://hdl.handle.net/11323/8572doi:10.32604/cmc.2021.017116Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/White 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.Muthumayil, K.Manikandan, S.Srinivasan, S.Escorcia-Gutierrez, Jose-will be generated-orcid-0000-0003-0518-3187-600Gamarra, Margarita-will be generated-orcid-0000-0003-1834-2984-600Mansour, Romany F.application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Computers, Materials & Continuahttps://www.techscience.com/cmc/v69n2/43859White blood cellsEnhanced virtual neural networkingSegmentationFeature extractionChronic lymphocytic leukemiaDiagnosis of leukemia disease based on enhanced virtual neural networkArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[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.[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.[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.[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.[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.[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.[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.[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.[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.[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.[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.[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.[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.[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.[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.[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.[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.[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.[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.[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.PublicationORIGINALDiagnosis of Leukemia Disease Based on Enhanced Virtual Neural Network.pdfDiagnosis of Leukemia Disease Based on Enhanced Virtual Neural Network.pdfapplication/pdf440266https://repositorio.cuc.edu.co/bitstreams/791366b6-759b-4079-871f-6220136988ee/downloadacd45d3cc22042385bc1972e62df7899MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/6f6c7ba8-c1fa-44d1-b481-abf6406357f9/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/66e01f99-a62d-4c5a-be80-67bc3b7110b9/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILDiagnosis of Leukemia Disease Based on Enhanced Virtual Neural Network.pdf.jpgDiagnosis of 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