Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network.

Leukocytes, also called White Blood Cells (WBCs) or leucocytes, are the cells that play a pivotal role in human health and are vital indicators of diseases such as malaria, leukemia, AIDS, and other viral infections. WBCs detection and classification in blood smears offers insights to pathologists,...

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Autores:: Khan, Siraj
Sajjad, Muhammad
Abbas, Naveed
Escorcia Gutierrez, José
Gamarra, Margarita
Muhammad, Khan

Tipo de recurso:: Article of investigation

Fecha de publicación:: 2024

Institución:: Corporación Universidad de la Costa

Repositorio:: REDICUC - Repositorio CUC

Idioma:: eng

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dc.title.eng.fl_str_mv	Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network.
title	Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network.
spellingShingle	Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network. Blood smear images Deep learning Dual attention network Image classification Image detection Leukocytes WBC classification
title_short	Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network.
title_full	Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network.
title_fullStr	Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network.
title_full_unstemmed	Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network.
title_sort	Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network.
dc.creator.fl_str_mv	Khan, Siraj Sajjad, Muhammad Abbas, Naveed Escorcia Gutierrez, José Gamarra, Margarita Muhammad, Khan
dc.contributor.author.none.fl_str_mv	Khan, Siraj Sajjad, Muhammad Abbas, Naveed Escorcia Gutierrez, José Gamarra, Margarita Muhammad, Khan
dc.subject.proposal.eng.fl_str_mv	Blood smear images Deep learning Dual attention network Image classification Image detection Leukocytes WBC classification
topic	Blood smear images Deep learning Dual attention network Image classification Image detection Leukocytes WBC classification
description	Leukocytes, also called White Blood Cells (WBCs) or leucocytes, are the cells that play a pivotal role in human health and are vital indicators of diseases such as malaria, leukemia, AIDS, and other viral infections. WBCs detection and classification in blood smears offers insights to pathologists, aiding diagnosis across medical conditions. Traditional techniques, including manual counting, detection, classification, and visual inspection of microscopic images by medical professionals, pose challenges due to their labor-intensive nature. However, traditional methods are time consuming and sometimes susceptible to errors. Here, we propose a high-performance convolutional neural network (CNN) coupled with a dual-attention network that efficiently detects and classifies WBCs in microscopic thick smear images. The main aim of this study was to enhance clinical hematology systems and expedite medical diagnostic processes. In the proposed technique, we utilized a deep convolutional generative adversarial network (DCGAN) to overcome the limitations imposed by limited training data and employed a dual attention mechanism to improve accuracy, efficiency, and generalization. The proposed technique achieved overall accuracy rates of 99.83%, 99.35%, and 99.60% for the peripheral blood cell (PBC), leukocyte images for segmentation and classification (LISC), and Raabin-WBC benchmark datasets, respectively. Our proposed approach outperforms state-of-the-art methods in terms of accuracy, highlighting the effectiveness of the strategies employed and their potential to enhance diagnostic capabilities and advance real-world healthcare practices and diagnostic systems.
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-10-29T19:38:24Z
dc.date.available.none.fl_str_mv	2024-10-29T19:38:24Z
dc.date.issued.none.fl_str_mv	2024-02-13
dc.type.none.fl_str_mv	Artículo de revista
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dc.identifier.citation.none.fl_str_mv	Siraj Khan, Muhammad Sajjad, Naveed Abbas, José Escorcia-Gutierrez, Margarita Gamarra, Khan Muhammad, Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network, Computers in Biology and Medicine, Volume 174, 2024, 108146, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2024.108146.
dc.identifier.issn.none.fl_str_mv	0010-4825
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/11323/13592
dc.identifier.doi.none.fl_str_mv	10.1016/j.compbiomed.2024.108146
dc.identifier.eissn.none.fl_str_mv	1879-0534
dc.identifier.instname.none.fl_str_mv	Corporación Universidad de la Costa
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identifier_str_mv	Siraj Khan, Muhammad Sajjad, Naveed Abbas, José Escorcia-Gutierrez, Margarita Gamarra, Khan Muhammad, Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network, Computers in Biology and Medicine, Volume 174, 2024, 108146, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2024.108146. 0010-4825 10.1016/j.compbiomed.2024.108146 1879-0534 Corporación Universidad de la Costa REDICUC - Repositorio CUC
url	https://hdl.handle.net/11323/13592 https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv	eng
language	eng
dc.relation.ispartofjournal.none.fl_str_mv	Computers in Biology and Medicine
dc.relation.references.none.fl_str_mv	M. Sajjad, et al. Leukocytes classification and segmentation in microscopic blood smear: a resource-aware healthcare service in smart cities IEEE Access, 5 (2016), pp. 3475-3489 S. Khan, M. Sajjad, T. Hussain, A. Ullah, A.S. Imran A review on traditional machine learning and deep learning models for WBCs classification in blood smear images IEEE Access, 9 (2020), pp. 10657-10673 L. Roland, M. Drillich, M. Iwersen Hematology as a diagnostic tool in bovine medicine J. Vet. Diagn. Invest., 26 (5) (2014), pp. 592-598 H. Kutlu, E. Avci, F. Özyurt White blood cells detection and classification based on regional convolutional neural networks Med. Hypotheses, 135 (2020), Article 109472 S. Hosseini, M.B. Khamesee Modeling and Simulation and Imaging of Blood Flow in the Human Body Nveo-Natural Volatiles & Essential Oils Journal\| NVEO (2021), pp. 13235-13244 Y. Yan, et al. Life-course cumulative burden of body mass index and blood pressure on progression of left ventricular mass and geometry in midlife: the Bogalusa Heart Study Circ. Res., 126 (5) (2020), pp. 633-643 B.J. Bain Structure and function of red and white blood cells and platelets Medicine, 49 (4) (2021), pp. 183-188 R.A. McPherson, M.R. Pincus Henry's Clinical Diagnosis and Management by Laboratory Methods E-Book Elsevier Health Sciences (2021) A.H. Alharbi, C. Aravinda, M. Lin, P. Venugopala, P. Reddicherla, M.A. Shah "Segmentation and classification of white blood cells using the UNet," Contrast Media Mol. Imaging, 2022 (2022) L. Alzubaidi, M.A. Fadhel, O. Al-Shamma, J. Zhang, Y. Duan Deep learning models for classification of red blood cells in microscopy images to aid in sickle cell anemia diagnosis Electronics, 9 (3) (2020), p. 427 M. Zhou, et al. Development and evaluation of a leukemia diagnosis system using deep learning in real clinical scenarios Frontiers in Pediatrics, 9 (2021), Article 693676 Variability in white blood cell count during uncomplicated malaria and implications for parasite density estimation: a WorldWide Antimalarial Resistance Network individual patient data meta-analysis Malar. J., 22 (1) (2023), p. 174 E. Rivas-Posada, M.I. Chacon-Murguia Automatic base-model selection for white blood cell image classification using meta-learning Comput. Biol. Med., 163 (2023), Article 107200 K. AL-Dulaimi, T. Makki Blood cell microscopic image classification in computer aided diagnosis using machine learning: a review Iraqi Journal For Computer Science and Mathematics, 4 (2) (2023), pp. 43-55 A. HemaSri, M.D. Sreenidhi, V.V.K. Chaitanya, G. Vasanth, V.M. Mohan, T. Satish Detection of RBCs, WBCs, platelets count in blood sample by using deep learning 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), IEEE (2023), pp. 47-51 S. Saleem, J. Amin, M. Sharif, G.A. Mallah, S. Kadry, A.H. Gandomi Leukemia segmentation and classification: a comprehensive survey Comput. Biol. Med. (2022), Article 106028 P.K. Das, V. Diya, S. Meher, R. Panda, A. Abraham A Systematic Review on Recent Advancements in Deep and Machine Learning Based Detection and Classification of Acute Lymphoblastic Leukemia IEEE access (2022) S. Khan, M. Sajjad, N. Abbas, A. Rehman A review on machine learning-based wbcs analysis in blood smear images: key challenges, datasets, and future directions Prognostic Models in Healthcare: AI and Statistical Approaches (2022), pp. 293-314 R.B. Hegde, K. Prasad, H. Hebbar, B.M.K. Singh Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images Biocybern. Biomed. Eng., 39 (2) (2019), pp. 382-392 H. Wu, C. Lin, J. Liu, Y. Song, Z. Wen, J. Qin Feature masking on non-overlapping regions for detecting dense cells in blood smear image IEEE Trans. Med. Imag. (2023) S.K. Pandey, A.K. Bhandari A systematic review of modern approaches in healthcare systems for lung cancer detection and classification Arch. Comput. Methods Eng. (2023), pp. 1-20 R.B. Hegde, K. Prasad, H. Hebbar, B.M.K.J.B. Singh, B. Engineering Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images 39 (2) (2019), pp. 382-392 M.R. Reena, P. Ameer Localization and recognition of leukocytes in peripheral blood: a deep learning approach Comput. Biol. Med., 126 (2020), Article 104034 Z. Han, et al. One-stage and lightweight CNN detection approach with attention: application to WBC detection of microscopic images Comput. Biol. Med., 154 (2023), Article 106606 B. Leng, C. Wang, M. Leng, M. Ge, W. Dong Deep learning detection network for peripheral blood leukocytes based on improved detection transformer Biomed. Signal Process Control, 82 (2023), Article 104518 W. Liu, et al. Ssd: single shot multibox detector Computer Vision–ECCV 2016: 14th European Conference, Springer, Amsterdam, The Netherlands (2016), pp. 21-37 October 11–14, 2016, Proceedings, Part I 14 S. Kadry, V. Rajinikanth, D. Taniar, R. Damaševičius, X.P.B. Valencia Automated segmentation of leukocyte from hematological images—a study using various CNN schemes J. Supercomput. (2022), pp. 1-21 S. Khan, M. Sajjad, T. Hussain, A. Ullah, A.S.J.I.A. Imran A review on traditional machine learning and deep learning models for WBCs classification in blood smear images 9 (2020), pp. 10657-10673 M. Sajjad, et al. Computer aided system for leukocytes classification and segmentation in blood smear images 2016 International Conference on Frontiers of Information Technology (FIT), IEEE (2016), pp. 99-104 S.N.M. Safuan, M.R.M. Tomari, W.N.W. Zakaria White blood cell (WBC) counting analysis in blood smear images using various color segmentation methods Measurement, 116 (2018), pp. 543-555 Z. Tang, D. Quan, X. Wang, N. Jin, D. Zhang Radar signal recognition based on dual-channel model with HOG feature extraction IEEE Journal on Miniaturization for Air and Space Systems (2023) M. Sajjad, et al. Raspberry Pi assisted face recognition framework for enhanced law-enforcement services in smart cities Future Generat. Comput. Syst., 108 (2020), pp. 995-1007 R.B. Hegde, K. Prasad, H. Hebbar, B.M.K. Singh Development of a robust algorithm for detection of nuclei and classification of white blood cells in peripheral blood smear images J. Med. Syst., 42 (2018), pp. 1-8 P. Rastogi, K. Khanna, V. Singh LeuFeatx: deep learning–based feature extractor for the diagnosis of acute leukemia from microscopic images of peripheral blood smear Comput. Biol. Med., 142 (2022), Article 105236 M.E. Sahin, H. Ulutas, E. Yuce, M.F. Erkoc Detection and classification of COVID-19 by using faster R-CNN and mask R-CNN on CT images Neural Comput. Appl., 35 (18) (2023), pp. 13597-13611 O. Katar, O. Yildirim An Explainable Vision Transformer Model Based White Blood Cells Classification and Localization (2023) K. Balasubramanian, N. Ananthamoorthy, K. Ramya An approach to classify white blood cells using convolutional neural network optimized by particle swarm optimization algorithm Neural Comput. Appl., 34 (18) (2022), pp. 16089-16101 J. Xu, H. Ren, S. Cai, X. Zhang An improved faster R-CNN algorithm for assisted detection of lung nodules Comput. Biol. Med., 153 (2023), Article 106470 H. Li, Y. Luo, J. Zhao, X. Luo, T.T. Toe Resnet for classification of leukocyte: application and optimization 2023 8th International Conference on Computer and Communication Systems (ICCCS), IEEE (2023), pp. 932-937 H. Chen, et al. Accurate classification of white blood cells by coupling pre-trained ResNet and DenseNet with SCAM mechanism BMC Bioinf., 23 (1) (2022), pp. 1-20 W. Sae-Lim, W. Wettayaprasit, P. Aiyarak Convolutional neural networks using MobileNet for skin lesion classification 2019 16th International Joint Conference on Computer Science and Software Engineering (JCSSE), IEEE (2019), pp. 242-247 K. Barrera, A. Merino, A. Molina, J. Rodellar Automatic generation of artificial images of leukocytes and leukemic cells using generative adversarial networks (syntheticcellgan) Comput. Methods Progr. Biomed., 229 (2023), Article 107314 K. Barrera, J. Rodellar, S. Alférez, A. Merino Automatic normalized digital color staining in the recognition of abnormal blood cells using generative adversarial networks Comput. Methods Progr. Biomed., 240 (2023), Article 107629 G.M. Devi, V. Neelambary Computer-aided diagnosis of white blood cell leukemia using VGG16 convolution neural network 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA), IEEE (2022), pp. 1064-1068 Y. Jing, Y. Yang, Z. Feng, J. Ye, Y. Yu, M. Song Neural style transfer: a review IEEE Trans. Visual. Comput. Graph., 26 (11) (2019), pp. 3365-3385 T. Bai, J. Luo, J. Zhao, B. Wen, Q. Wang Recent advances in adversarial training for adversarial robustness arXiv preprint arXiv (2021) 2102.01356 L. Gonog, Y. Zhou A review: generative adversarial networks 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), IEEE (2019), pp. 505-510 H. Nozaka, K. Kamata, K. Yamagata The effectiveness of data augmentation for mature white blood cell image classification in deep learning—selection of an optimal technique for hematological morphology recognition IEICE Trans. Info Syst., 106 (5) (2023), pp. 707-714 C. Shorten, T.M. Khoshgoftaar A survey on image data augmentation for deep learning Journal of big data, 6 (1) (2019), pp. 1-48 X. Gao, F. Deng, X. Yue Data augmentation in fault diagnosis based on the Wasserstein generative adversarial network with gradient penalty Neurocomputing, 396 (2020), pp. 487-494 A. Bissoto, E. Valle, S. Avila Gan-based data augmentation and anonymization for skin-lesion analysis: a critical review Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2021), pp. 1847-1856 A. Mert Enhanced dataset synthesis using conditional generative adversarial networks Biomedical Engineering Letters, 13 (1) (2023), pp. 41-48 M. Mirza, S. Osindero Conditional generative adversarial nets arXiv preprint arXiv (2014) 1411.1784 C. Ledig, et al. Photo-realistic single image super-resolution using a generative adversarial network Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 4681-4690 J.-Y. Zhu, T. Park, P. Isola, A.A. Efros Unpaired image-to-image translation using cycle-consistent adversarial networks Proceedings of the IEEE International Conference on Computer Vision (2017), pp. 2223-2232 X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, P. Abbeel Infogan: interpretable representation learning by information maximizing generative adversarial nets Adv. Neural Inf. Process. Syst., 29 (2016) M. Arjovsky, S. Chintala, L. Bottou Wasserstein generative adversarial networks International Conference on Machine Learning, PMLR (2017), pp. 214-223 C. Hartanto, S. Kurniawan, D. Arianto, A. Arymurthy Dcgan-generated synthetic images effect on white blood cell classification IOP Conf. Ser. Mater. Sci. Eng., 1077 (1) (2021), Article 012033 IOP Publishing Z. Qin, Z. Liu, P. Zhu, Y. 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Rodellar A dataset of microscopic peripheral blood cell images for development of automatic recognition systems Data Brief, 30 (2020) S.H. Rezatofighi, H. Soltanian-Zadeh Automatic recognition of five types of white blood cells in peripheral blood Comput. Med. Imag. Graph., 35 (4) (2011), pp. 333-343 Z.M. Kouzehkanan, et al. A large dataset of white blood cells containing cell locations and types, along with segmented nuclei and cytoplasm Sci. Rep., 12 (1) (2022), p. 1123 D. Hazra, Y.-C. Byun, W.J. Kim, C.-U. Kang Synthesis of microscopic cell images obtained from bone marrow aspirate smears through generative adversarial networks Biology, 11 (2) (2022), p. 276 D. Hazra, Y.-C. Byun, W.J. Kim Enhancing classification of cells procured from bone marrow aspirate smears using generative adversarial networks and sequential convolutional neural network Comput. Methods Progr. Biomed., 224 (2022), Article 107019 S.S.R. Bairaboina, S.R. Battula Ghost-ResNeXt: an effective deep learning based on mature and immature WBC classification Appl. Sci., 13 (6) (2023), p. 4054 R. Liu, W. Dai, T. Wu, M. Wang, S. Wan, J. Liu AIMIC: deep learning for microscopic image classification Comput. Methods Progr. Biomed., 226 (2022), Article 107162 R. Zhang, et al. RCMNet: a deep learning model assists CAR-T therapy for leukemia Comput. Biol. Med., 150 (2022), Article 106084 Q. Wang, S. Bi, M. Sun, Y. Wang, D. Wang, S. Yang Deep learning approach to peripheral leukocyte recognition PLoS One, 14 (6) (2019), Article e0218808 K. Kimura, et al. A novel automated image analysis system using deep convolutional neural networks can assist to differentiate MDS and AA Sci. Rep., 9 (1) (2019), Article 13385 C. Jung, M. Abuhamad, D. Mohaisen, K. Han, D. Nyang WBC image classification and generative models based on convolutional neural network BMC Med. Imag., 22 (1) (2022), pp. 1-16 K. Balasubramanian, K. Gayathri Devi, and K. Ramya, "Classification of white blood cells based on modified U‐Net and SVM," Concurrency Comput. Pract. Ex., p. e7862. E. Rivas-Posada, M.I. Chacon-Murguia, J.A. Ramírez-Quintana, C. Arzate-Quintana Classification of leukocytes using meta-learning and color constancy methods Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), 8 (4) (2022), pp. 486-499 B.S.S. Rao, B.S. Rao An effective WBC segmentation and classification using MobilenetV3–ShufflenetV2 based deep learning framework IEEE Access, 11 (2023), pp. 27739-27748
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spelling	Atribución 4.0 Internacional (CC BY 4.0)© 2024https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Khan, SirajSajjad, MuhammadAbbas, NaveedEscorcia Gutierrez, JoséGamarra, MargaritaMuhammad, Khan2024-10-29T19:38:24Z2024-10-29T19:38:24Z2024-02-13Siraj Khan, Muhammad Sajjad, Naveed Abbas, José Escorcia-Gutierrez, Margarita Gamarra, Khan Muhammad, Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network, Computers in Biology and Medicine, Volume 174, 2024, 108146, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2024.108146.0010-4825https://hdl.handle.net/11323/1359210.1016/j.compbiomed.2024.1081461879-0534Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Leukocytes, also called White Blood Cells (WBCs) or leucocytes, are the cells that play a pivotal role in human health and are vital indicators of diseases such as malaria, leukemia, AIDS, and other viral infections. WBCs detection and classification in blood smears offers insights to pathologists, aiding diagnosis across medical conditions. Traditional techniques, including manual counting, detection, classification, and visual inspection of microscopic images by medical professionals, pose challenges due to their labor-intensive nature. However, traditional methods are time consuming and sometimes susceptible to errors. Here, we propose a high-performance convolutional neural network (CNN) coupled with a dual-attention network that efficiently detects and classifies WBCs in microscopic thick smear images. The main aim of this study was to enhance clinical hematology systems and expedite medical diagnostic processes. In the proposed technique, we utilized a deep convolutional generative adversarial network (DCGAN) to overcome the limitations imposed by limited training data and employed a dual attention mechanism to improve accuracy, efficiency, and generalization. The proposed technique achieved overall accuracy rates of 99.83%, 99.35%, and 99.60% for the peripheral blood cell (PBC), leukocyte images for segmentation and classification (LISC), and Raabin-WBC benchmark datasets, respectively. Our proposed approach outperforms state-of-the-art methods in terms of accuracy, highlighting the effectiveness of the strategies employed and their potential to enhance diagnostic capabilities and advance real-world healthcare practices and diagnostic systems.17 páginasapplication/pdfengElsevier LtdUnited Kingdomhttps://www.sciencedirect.com/science/article/pii/S0010482524002300?via%3DihubEfficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network.Artículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Computers in Biology and MedicineM. Sajjad, et al. Leukocytes classification and segmentation in microscopic blood smear: a resource-aware healthcare service in smart cities IEEE Access, 5 (2016), pp. 3475-3489S. Khan, M. Sajjad, T. Hussain, A. Ullah, A.S. Imran A review on traditional machine learning and deep learning models for WBCs classification in blood smear images IEEE Access, 9 (2020), pp. 10657-10673L. Roland, M. Drillich, M. Iwersen Hematology as a diagnostic tool in bovine medicine J. Vet. Diagn. Invest., 26 (5) (2014), pp. 592-598H. Kutlu, E. Avci, F. Özyurt White blood cells detection and classification based on regional convolutional neural networks Med. Hypotheses, 135 (2020), Article 109472S. Hosseini, M.B. Khamesee Modeling and Simulation and Imaging of Blood Flow in the Human Body Nveo-Natural Volatiles & Essential Oils Journal\| NVEO (2021), pp. 13235-13244Y. Yan, et al. Life-course cumulative burden of body mass index and blood pressure on progression of left ventricular mass and geometry in midlife: the Bogalusa Heart Study Circ. Res., 126 (5) (2020), pp. 633-643B.J. Bain Structure and function of red and white blood cells and platelets Medicine, 49 (4) (2021), pp. 183-188R.A. 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Rao An effective WBC segmentation and classification using MobilenetV3–ShufflenetV2 based deep learning framework IEEE Access, 11 (2023), pp. 27739-27748171108146174Blood smear imagesDeep learningDual attention networkImage classificationImage detectionLeukocytesWBC classificationPublicationORIGINALEfficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network..pdfEfficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network..pdfapplication/pdf8218923https://repositorio.cuc.edu.co/bitstreams/a341a8a2-0de1-4731-872f-486b4afbef37/download7a4e4610c8d54ecd0d19a909b259eb15MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-815543https://repositorio.cuc.edu.co/bitstreams/7674122f-e904-400d-86f1-543687461e73/download73a5432e0b76442b22b026844140d683MD52TEXTEfficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network..pdf.txtEfficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network..pdf.txtExtracted texttext/plain100409https://repositorio.cuc.edu.co/bitstreams/61949393-63fa-4191-a7fa-a41756e4521f/download6f138c06887040ebfa67a148dbfb5facMD53THUMBNAILEfficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network..pdf.jpgEfficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network..pdf.jpgGenerated Thumbnailimage/jpeg14247https://repositorio.cuc.edu.co/bitstreams/15a60c40-a231-426c-a07a-281d52768ccc/downloadae9f786d6d497e799d03110e063ccbd1MD5411323/13592oai:repositorio.cuc.edu.co:11323/135922024-10-30 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ara ejercer estos derechos sobre la Obra tal y como se indica a continuación:</p>
    <ol type="a">
      <li>Reproducir la Obra, incorporar la Obra en una o más Obras Colectivas, y reproducir la Obra incorporada en las Obras Colectivas.</li>
      <li>Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.</li>
      <li>Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.</li>
    </ol>
    <p>Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).</p>
  </li>
  <br/>
  <li>
    Restricciones.
    <p>La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:</p>
    <ol type="a">
      <li>Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).</li>
      <li>Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.</li>
      <li>Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.</li>
      <li>
        Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:
        <ol type="i">
          <li>Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.</li>
          <li>Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
        </ol>
      </li>
      <li>Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
    </ol>
  </li>
  <br/>
  <li>
    Representaciones, Garantías y Limitaciones de Responsabilidad.
    <p>A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Limitación de responsabilidad.
    <p>A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Término.
    <ol type="a">
      <li>Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.</li>
      <li>Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.</li>
    </ol>
  </li>
  <br/>
  <li>
    Varios.
    <ol type="a">
      <li>Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.</li>
      <li>Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.</li>
      <li>Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.</li>
      <li>Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.</li>
    </ol>
  </li>
  <br/>
</ol>


Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network.

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