Relevant bibliographies by topics / Microscopic blood images

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Microscopic blood images'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Microscopic blood images"

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R, Saravanakumar, Kiruthiga G, Lubna K, Nivedha P, and Pavithra K. "Analysis Of Microscopic Blood Images for Detecting Leukemia using Nuclear Segmentation." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 05, no. 04 (2017): 04–07. http://dx.doi.org/10.9756/sijcsea/v5i4/05010180101.

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Mohammed, Zhana Fidakar, and Alan Anwer Abdulla. "Thresholding-based White Blood Cells Segmentation from Microscopic Blood Images." UHD Journal of Science and Technology 4, no. 1 (2020): 9. http://dx.doi.org/10.21928/uhdjst.v4n1y2020.pp9-17.

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Digital image processing has a significant role in different research areas, including medical image processing, object detection, biometrics, information hiding, and image compression. Image segmentation, which is one of the most important steps in processing medical image, makes the objects inside images more meaningful. For example, from microscopic images, blood cancer can be identified which is known as leukemia; for this purpose at first, the white blood cells (WBCs) need to be segmented. This paper focuses on developing a segmentation technique for segmenting WBCs from microscopic blood
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M S, Vidyashree. "Detection of Blood Cancer Cells Using Microscopic Images." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 995–1003. http://dx.doi.org/10.22214/ijraset.2021.37536.

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Abstract: Blood Cancer cells forming a tissue is called lymphoma. Thus, disease decreases the cells to fight against the infection or cancer blood cells. Blood cancer is also categorized in too many types. The two main categories of blood cancer are Acute Lymphocytic Lymphoma and Acute Myeloid Lymphoma. In this project proposes a approach that robotic detects and segments the nucleolus from white blood cells in the microscopic Blood images. Here in this project, we have used the two Machine learning algorithms that are k-means algorithm, Support vector machine algorithm. K-mean algorithm is us
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Kirubakaran, J., P. Vedhanath, P. Lakshman, and Y. Surya Ashok. "Detection of Blood Cancer Cells using Microscopic Image." June 2024 6, no. 2 (2024): 164–73. http://dx.doi.org/10.36548/jiip.2024.2.007.

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For the automatic diagnosis and classification of leukemia and leukemoid reactions, the IDB2 (acute lymphoblastic leukemia-image database) dataset has been utilized. This paper focuses on an automated method to differentiate between leukemoid and leukemia reactions using images of blood smear. MobileNetV3 is employed to classify and count WBC types from segmented images. The BCCD (Blood Cell Count Detection) dataset, which contains 364 images of blood smear and 349 single WBC type images, has been used in this work. The image segmentation algorithm incorporates Fuzzy C-means clustering, the sn
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Rajaraman, Ajay. "An Automated Detection of Leukemia." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 1977–81. http://dx.doi.org/10.22214/ijraset.2021.37670.

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Abstract: Currently, the identification of blood disorders is through visual inspection of microscopic images of the blood cells. The identification of blood disorders can lead to the classification of certain diseases related to blood. This paper describes a preliminary study of developing the detection of leukemia types using microscopic blood sample images. Analyzing through images is very important because diseases can be detected and diagnosed at an earlier stage. From there further actions like controlling, monitoring, and prevention of diseases can be done. Keywords: Image processing; l
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Bouzid-Daho, Abdellatif, Naima Sofi, Schahrazad Soltane, and Patrick Siarry. "Automated detection in microscopic images using segmentation." Brazilian Journal of Technology 7, no. 2 (2024): e69317. http://dx.doi.org/10.38152/bjtv7n2-003.

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In this paper, we present a segmentation clusteringbased approach for automated object detection. This paper deals with the segmentation and classification of blood cells for the purpose of detecting leukemia (abnormal blood cells). After the image acquisition and the preprocessing step, we proceeded to the application of the k-means method. In order to show the interest of the proposed approach, we present the different cancerous regions identified with their characteristics for biomedical diagnostic aid. The proposed method is tested on image dataset and achieves 98% segmentation accuracy. T
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Vishlesh, V. Poojary, and Niranjana Sampathila. "Screening of Leukemia from Microscopic Images of blood Smear." Indian Journal of Public Health Research & Development 10, no. 5 (2019): 197. http://dx.doi.org/10.5958/0976-5506.2019.00996.3.

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Razzak, Muhammad Imran, and Bandar Alhaqbani. "Automatic Detection of Malarial Parasite Using Microscopic Blood Images." Journal of Medical Imaging and Health Informatics 5, no. 3 (2015): 591–98. http://dx.doi.org/10.1166/jmihi.2015.1417.

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Huffman, Scott W., Kara B. Lukasiewicz, and Chris W. Brown. "FTIR Hyperspectral Images of Microscopic Droplets of Splattered Blood." Microscopy Today 11, no. 3 (2003): 10–15. http://dx.doi.org/10.1017/s1551929500052615.

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During forensic Investigations, determining the time frame of a crime can be an extremely important clue for solving the case. The exact time at which a crime was committed can be especially difficult to determine when considerable time has elapsed. To improve the predictive capabilities of crime scene investigators, we have focused on using spectroscopic methods to Investigate the aging of bloodstains.
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Fatima, Tehreem, and Muhammad Shahid Farid. "Automatic detection of Plasmodium parasites from microscopic blood images." Journal of Parasitic Diseases 44, no. 1 (2019): 69–78. http://dx.doi.org/10.1007/s12639-019-01163-x.

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Dissertations / Theses on the topic "Microscopic blood images"

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LODDO, ANDREA. "Microscopic Blood Images Analysis by Computer Vision Techniques." Doctoral thesis, Università degli Studi di Cagliari, 2019. http://hdl.handle.net/11584/261571.

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Automatic analysis and information extraction from an image is still a highly challenging research problem in the computer vision area, attempting to describe the image content with computational and mathematical techniques. Moreover, the information extracted from the image should be meaningful and as most discriminatory as possible, since it will be used to categorize its content according to the analyzed problem. In the Medical Imaging domain, many important decisions that affect patient care depends on the usefulness of the information extracted from the image. Managing medical image is ev
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Hassini, Houda. "Automatic analysis of blood smears images : contribution of phase modality in Fourier Ptychographic Microscopy." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS014.

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La pathologie numérique constitue aujourd'hui un outil fondamental pour le diagnostic médical, exploitant les avancées technologiques en matière de numérisation pour transformer les échantillons biologiques en données numériques, facilitant ainsi leur visualisation et leur analyse. Cependant, ces méthodes, souvent basées sur la microscopie conventionnelle, rencontrent des limitations qui entravent parfois leur efficacité. Dans ce contexte, des méthodes d'imagerie non conventionnelles telles que la microscopie ptychographique de Fourier (FPM) offrent des perspectives prometteuses pour surmonter
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Naidoo, Thegaran. "Digital holographic microscopy with automated detection of red blood cells." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/61032.

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The digital in-line holographic configuration is motivated by the goal of developing a portable, cost effective sensor system for pre-screening patient blood samples. The theory of holography is explained from the foundational concepts in scalar diffraction theory all the way through to the implementation of reconstruction algorithms. Methods for the enhancement of holographic reconstructions are described. The algorithms that perform an automated count of the reconstructed objects are described and demonstrated. Simulated and experimental results are provided. Together, the lens-free holograp
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Bouchama, Lyes. "Apport des techniques d'apprentissage (profond) à la microscopie holographique pour applications médicales." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAS022.

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Mon travail s'inscrit dans le cadre du partenariat stratégique Télécom SudParis (TSP) et TRIBVN/T-life, dédié au développement de nouvelles approches en microscopie optique, couplées à l'intelligence artificielle, en vue d'identifier, de prédire et de monitorer les pathologies hématologiques et parasitologiques. C'est dans cette perspective que nous avons développé, dans le laboratoire, un prototype de microscope reposant sur un principe d'imagerie non conventionnelle à synthèse d'ouverture, basée sur l'approche FPM (Fourier Ptychographic Microscopy). Cette approche permet de dépasser les limi
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Toschka, Robert. "Identification of human peripheral blood monocyte derived pro-inflammatory dendritic cells." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2014. http://dx.doi.org/10.18452/17040.

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Dendritische Zellen (DZ) sind essentiell für die Aktivierung von Immunantworten. Drei Flt3-abhängige DZ Populationen aus dem Blut bestehend aus konventionellen (kDZ) BDCA1+ DZs und BDCA3+ DZs und plasmazytoide DZs wurden bereits beschrieben. Hier wurden zum ersten Mal sich aus Monozyten entwickelnde DZ (moDZ), genauer BDCA1+CD14+ pro-inflammatorische DZ (pro-iDZ) aus periphärem Blut unter homöostatischen Bedingungen identifiziert. Isolierte pro-iDZ sekretierten spontan große Mengen an pro-inflammatorischen Zytokinen, die kDZ reifen ließen und T Zell Proliferation unterstützten. Sie waren BDCA1
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Andersson, Vidar. "Evaluation of CellaVision DM1200 Vet and its ability to differentiate feline leukocytes compared to manual differential count and Advia 2120." Thesis, Uppsala universitet, Institutionen för kvinnors och barns hälsa, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-295630.

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Leukocyte differential count in peripheral blood smear has, ever since the method was developed more than 100 years ago, been one of the most frequently used diagnostics tool in the routine hematology laboratory. The manual differential count of leukocytes using a microscope is still the standard method in most small and medium sized laboratories. Even though the method does not require any expensive instruments it comes at a high cost due to it being labor intensive and time consuming. In recent years the rapid technical advancements has led to the development of automatic or semi-automatic m
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Siddiqui, Faiza. "White Blood Cells Identification and Classification from Microscopic Blood Images." Thesis, 2016. http://ethesis.nitrkl.ac.in/9122/1/2016_MT_FSiddiqui.pdf.

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Identification of issues related to blood can be done by the visible assessment of components microscopic images present in the blood. From the recognition of blood issue, it can prompt the identification and classification of many diseases relevant to blood. Analalysis of white blood cells of allows for the detection of Acute Lymphoblastic leukemia, it is a type of blood cancer that can result in death if not treated and detected early. In this work, a system is proposed for identification and classification of white blood cells using microsccopic images of blood. This process can also be p
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Chen, Hsuen-Hui, and 陳雪惠. "Capillary Blood Flow Measurement Based on Nailfold Microscopic Images." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/85004257196800051554.

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碩士<br>中原大學<br>電機工程研究所<br>90<br>Microscopic video images of microcirculation have been used in clinical diagnosis for years, and the parameters obtained from images reveal most physiological activities and body organizations. Particularly, the blood flow speed is one of important indexes, which reflects the state of microcirculation and means very much in diagnosis. Because the microscopic video images of microcirculation from finger nailfold are easily influenced by some external factors such as trembling which incurs an inferior image and other problems, dynamic microscopic video images match
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Hanke, Jana. "The Influence of Substrate Elasticity and Shear Rate on Human Blood Platelet Contraction." Doctoral thesis, 2018. http://hdl.handle.net/11858/00-1735-0000-002E-E4A3-4.

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Books on the topic "Microscopic blood images"

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Press, Trula Book. Process Virus Transmission Viruses: Cough, Medical Mask, Hospital, Cold, No Handshake, Blood Test, Microscope, Mosquito for Teenage Girls Image Quiz Words Activity Coloring Books 55 Image. Independently Published, 2020.

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Youssef, Keiko. Indirect Virus Transmission: Shower, Search, Global Economy, Virus, Microscope, Search, Blood Test, Virus for Kids Ages 3-5 Image Quizzes Words Activity Coloring Books 40 Fun. Independently Published, 2020.

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Book chapters on the topic "Microscopic blood images"

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Selvanathan, N., Lee Shi Yun, Mangalam Sankupellay, V. Purushothaman, and S. Jameelah. "Automatic Retrieval of Microscopic Blood Cells Images." In 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-68017-8_64.

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Ananth, Christo, P. Tamilselvi, S. Agnes Joshy, and T. Ananth Kumar. "Blood Cancer Detection with Microscopic Images Using Machine Learning." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5090-2_4.

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Mansouri, Hadia, and Faouzi Benzarti. "Analysis of Blood Smear Microscopic Images Using ML: DL." In Proceedings of the Second International Conference on Advances in Computing Research (ACR’24). Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56950-0_37.

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Navya, K. T., Subhraneil Das, and Keerthana Prasad. "Automatic Segmentation of Red Blood Cells from Microscopic Blood Smear Images Using Image Processing Techniques." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9967-2_5.

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Alam, Afrin, and Shamama Anwar. "Detecting Acute Lymphoblastic Leukemia Through Microscopic Blood Images Using CNN." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6393-9_22.

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Mundhra, Dheeraj, Bharath Cheluvaraju, Jaiprasad Rampure, and Tathagato Rai Dastidar. "Analyzing Microscopic Images of Peripheral Blood Smear Using Deep Learning." In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67558-9_21.

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Somasekar, J., A. Rama Mohan Reddy, and L. Sreenivasulu Reddy. "An Efficient Algorithm for Automatic Malaria Detection in Microscopic Blood Images." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29216-3_47.

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Theera-Umpon, Nipon. "White Blood Cell Segmentation and Classification in Microscopic Bone Marrow Images." In Fuzzy Systems and Knowledge Discovery. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11540007_98.

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Asha, S. B., and G. Gopakumar. "Deep Learning-Based Semantic Segmentation of Blood Cells from Microscopic Images." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3481-2_30.

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Pavani, Bhupathi Vishva, Gaddam Thanmai, and Rimjhim Padam Singh. "Efficient Detection of Acute Lymphoblastic Leukemia in Microscopic Blood Cell Images." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-81821-9_1.

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Conference papers on the topic "Microscopic blood images"

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Lekeufack Foulefack, Rosmaël Zidane, Andronicus A. Akinyelu, Dinna Ranirina, and Berthine N. Mpinda. "Malaria Parasite Detection in Microscopic Blood Smear Images Using Deep Learning Techniques." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651385.

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Pujitha, M. Vani, Srinija Srikantam, Ch Sahithi, and Akhila Timmasarti. "Automated Leukemia Diagnosis from Microscopic Blood Smear Images Using Adapted Inception V3." In 2024 International Conference on Big Data Analytics in Bioinformatics (DABCon). IEEE, 2024. https://doi.org/10.1109/dabcon63472.2024.10919430.

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Ooko, Samson Otieno, and Charles Theuri Kagwi. "A Machine Learning Model for Prediction of Malaria from Microscopic Blood Cell Images." In 2024 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics. IEEE, 2024. http://dx.doi.org/10.1109/ithings-greencom-cpscom-smartdata-cybermatics62450.2024.00060.

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Sasikala, S., S. Sureshkannan, S. Elango, Sundara Rajulu Navaneethakrishnan, R. Praveena, and T. R. Ganesh Babu. "Enhancing White Blood Cell Detection in Microscopic Images Through Segmentation and Fusion Methodologies." In 2024 9th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2024. https://doi.org/10.1109/icces63552.2024.10859562.

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Manoj, Milan, Vishnu Sreekumar, Surya Reghuram, Anjali T, and Nandakishor Prabhu Ramlal. "Machine Learning-Based Detection of Blood Cancer from Microscopic Images: A Comparative Study." In 2024 9th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2024. https://doi.org/10.1109/icces63552.2024.10859403.

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Wang, Cheng, Yuichiro Hayashi, Masahiro Oda, Shuntaro Kawamura, Takanori Takebe, and Kensaku Mori. "Prior knowledge-based blood vessel reconstruction and thrombus visualization using confocal laser scanning microscopic images." In Clinical and Biomedical Imaging, edited by Barjor S. Gimi and Andrzej Krol. SPIE, 2025. https://doi.org/10.1117/12.3048339.

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Alkhouli, Mahmoud Saed, and Hiren Joshi. "Trans-SegNet - Deep Transfer Learning Approach to Detect Abnormalities in Microscopic Blood Smear Images for Medical Image Segmentation." In 2024 5th International Conference on Communication, Computing & Industry 6.0 (C2I6). IEEE, 2024. https://doi.org/10.1109/c2i663243.2024.10895701.

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Kyeremah, Charlotte, Aditya Paul, Daniel Haehn, Manoj T. Duraisingh, and Chandra S. Yelleswarapu. "Phase-support Constraint for Twin-Image Suppression and Phase-Based Classification of Malaria-Infected Red Blood Cells." In Novel Techniques in Microscopy. Optica Publishing Group, 2025. https://doi.org/10.1364/ntm.2025.nth2c.1.

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We developed a phase-support constraint algorithm to suppress twin-image artifacts. We achieved higher parasitemia detection using the optical phase as a classifier compared to features like surface area, which offers reliable holographic microscopy solutions for diagnostics and biomedical imaging.
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Savkare, S. S., and S. P. Narote. "Blood cell segmentation from microscopic blood images." In 2015 International Conference on Information Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/infop.2015.7489435.

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Mohapatra, S., D. Patra, and S. Satpathi. "Image analysis of blood microscopic images for acute leukemia detection." In 2010 International Conference on Industrial Electronics, Control and Robotics (IECR). IEEE, 2010. http://dx.doi.org/10.1109/iecr.2010.5720171.

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