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Journal articles on the topic 'Microscopic blood images'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

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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Manthouri, Mohammad, Zhila Aghajari, and Sheida Safary. "Computational Intelligence Method for Detection of White Blood Cells Using Hybrid of Convolutional Deep Learning and SIFT." Computational and Mathematical Methods in Medicine 2022 (January 12, 2022): 1–8. http://dx.doi.org/10.1155/2022/9934144.

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Infection diseases are among the top global issues with negative impacts on health, economy, and society as a whole. One of the most effective ways to detect these diseases is done by analysing the microscopic images of blood cells. Artificial intelligence (AI) techniques are now widely used to detect these blood cells and explore their structures. In recent years, deep learning architectures have been utilized as they are powerful tools for big data analysis. In this work, we are presenting a deep neural network for processing of microscopic images of blood cells. Processing these images is p

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Fatichah, Chastine, Diana Purwitasari, Victor Hariadi, and Faried Effendy. "OVERLAPPING WHITE BLOOD CELL SEGMENTATION AND COUNTING ON MICROSCOPIC BLOOD CELL IMAGES." International Journal on Smart Sensing and Intelligent Systems 7, no. 3 (2014): 1271–86. http://dx.doi.org/10.21307/ijssis-2017-705.

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Ukolova, A. V., D. V. Bykov, M. A. Akimushkina, et al. "Using an artificial neural network to recognize sturgeon blood cells in microscopic images." Timiryazev Biological Journal 2, no. 4 (2025): 83–93. https://doi.org/10.26897/2949-4710-2024-2-4-83-93.

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The article considers the labor intensity of the process of determining the white blood cell count of fish, which is simultaneously being highly significant and necessary in terms of monitoring the health of individuals. The authors present an approach to automating the compilation of the white blood cell count of fish (using sturgeon as an example) using a convolutional neural network model capable of recognizing and identifying cells in a microscopic blood image. The general scheme of hematopoiesis and standards for hematological parameters of sturgeon are considered. The procedure for prepa

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Loey, Mohamed, Mukdad Naman, and Hala Zayed. "Deep Transfer Learning in Diagnosing Leukemia in Blood Cells." Computers 9, no. 2 (2020): 29. http://dx.doi.org/10.3390/computers9020029.

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Leukemia is a fatal disease that threatens the lives of many patients. Early detection can effectively improve its rate of remission. This paper proposes two automated classification models based on blood microscopic images to detect leukemia by employing transfer learning, rather than traditional approaches that have several disadvantages. In the first model, blood microscopic images are pre-processed; then, features are extracted by a pre-trained deep convolutional neural network named AlexNet, which makes classifications according to numerous well-known classifiers. In the second model, aft

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Younis, Ahmed Khazal, Basma Mohammed Kamal Younis, and Mohammed Sabah Jarjees. "Hardware implementation of Sobel edge detection system for blood cells images-based field programmable gate array." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (2022): 86–95. https://doi.org/10.11591/ijeecs.v26.i1.pp86-95.

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The microscopic-blood image has been used to diagnose various diseases according to the morphological specifications of red and white blood cells. However, the manual analysis and procedures are not accurate due to the human error. Therefore, several studies conducted to find new techniques to perform this analysis using computer algorithms. The complexity of these algorithms led to thinking in simpler ways or to the hardware solutions. On the other hand, edge detection is a mathematical procedure that play an essential role in the field of medical image processing. It is considered as one of

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Wapalila, Honest, Shubi Kaijage, and Judith Leo. "Identifying Requirements for Enhanced Deep Learning Classification in Malaria Microscopic Images Analysis." Indian Journal Of Science And Technology 17, no. 42 (2024): 4372–79. http://dx.doi.org/10.17485/ijst/v17i42.2727.

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Objective: The major goal of the study is to improve malaria diagnosis by applying advanced deep learning models, improving the model architecture, increasing dataset quality, and complex image preprocessing methodologies for more correct blood smear categorization. Methods: Blood smear images, covering both malaria-positive and negative microscopic images, were aggregated and pre-processed to assure dataset quality, employing techniques such as image standardization, noise reduction, and contrast enhancement. The study has analyzed the dataset's size and quality in order to maximize deep lear

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Sivasangari, A., and G. Sasikumar. "Detection of Leukemia Using Image Processing." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 4 (2018): 61. http://dx.doi.org/10.23956/ijarcsse.v8i4.632.

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Leukemia disease is one of the leading causes of death among human. Its cure rate and prognosis depends mainly on the early detection and diagnosis of the disease. At the moment, identification of blood disorders is through visual inspection of microscopic images by examining changes like texture, geometry, colour and statistical analysis of images . This project aims to preliminary of developing a detection of leukemia types using microscopic blood sample using MATLAB. Images are used as they are cheap and do not expensive for testing and lab equipment.

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Ei, Ei Chaw, and Win Ohnmar. "Classification of Leukemia Detection in Human Blood Sample Based on Microscopic Images." International Journal of Trend in Scientific Research and Development 3, no. 4 (2019): 1470–74. https://doi.org/10.5281/zenodo.3591248.

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Nowadays, the automatic specific tests such as Cytogenetics, Immunophenotyping and morphological cell classification can identify the leukemia disease by making experienced operators observing blood or bone marrow microscopic images. The early identification of Acute Lymphoblastic Leukemia ALL symptoms in patients can greatly increase the probability of recovery. When typical symptoms appear in normal blood analysis, those methods are not included into large screening programs and are applied only. The method of blood cell observation using Cytogenetics and Immunophenotyping diagnostic methods

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Zahran, Bilal Mohammad, Belal A. Ayyoub, Rushdi S. Abu Zunait, and A. Sharadqeh. "Automated Blood Cells Extraction and counting." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 9 (2014): 3906–11. http://dx.doi.org/10.24297/ijct.v12i9.2831.

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Automated blood cells extraction and counting in medical images is an important and sometimes necessary procedure in medical diagnostics and treatment. In this paper an automated blood cells extraction and counting method from microscopic images was proposed. The method is based on morphological filtering. The proposed method was tested using different background noises, resolutions and different colored images. The results are encouraging and the method gave an excellent performance for white blood cells and acceptable performance for red blood cells.

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20

Bhukya, et al. "Detection of acute lymphoblastic leukemia using microscopic images of blood." International Journal of ADVANCED AND APPLIED SCIENCES 4, no. 8 (2017): 74–78. http://dx.doi.org/10.21833/ijaas.2017.08.011.

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21

Maqsood, Asma, Muhammad Shahid Farid, Muhammad Hassan Khan, and Marcin Grzegorzek. "Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images." Applied Sciences 11, no. 5 (2021): 2284. http://dx.doi.org/10.3390/app11052284.

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Malaria is a disease activated by a type of microscopic parasite transmitted from infected female mosquito bites to humans. Malaria is a fatal disease that is endemic in many regions of the world. Quick diagnosis of this disease will be very valuable for patients, as traditional methods require tedious work for its detection. Recently, some automated methods have been proposed that exploit hand-crafted feature extraction techniques however, their accuracies are not reliable. Deep learning approaches modernize the world with their superior performance. Convolutional Neural Networks (CNN) are va

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Shaheen, Maneela, Rafiullah Khan, R. R. Biswal, et al. "Acute Myeloid Leukemia (AML) Detection Using AlexNet Model." Complexity 2021 (May 28, 2021): 1–8. http://dx.doi.org/10.1155/2021/6658192.

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Acute Myeloid Leukemia (AML) is a kind of fatal blood cancer with a high death rate caused by abnormal cells’ rapid growth in the human body. The usual method to detect AML is the manual microscopic examination of the blood sample, which is tedious and time-consuming and requires a skilled medical operator for accurate detection. In this work, we proposed an AlexNet-based classification model to detect Acute Myeloid Leukemia (AML) in microscopic blood images and compared its performance with LeNet-5-based model in Precision, Recall, Accuracy, and Quadratic Loss. The experiments are conducted o

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Wang, Xiangzhou, Lin Liu, Xiaohui Du, Jing Zhang, Guangming Ni, and Juanxiu Liu. "GMANet: Gradient Mask Attention Network for Finding Clearest Human Fecal Microscopic Image in Autofocus Process." Applied Sciences 11, no. 21 (2021): 10293. http://dx.doi.org/10.3390/app112110293.

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The intelligent recognition of formed elements in microscopic images is a research hotspot. Whether the microscopic image is clear or blurred is the key factor affecting the recognition accuracy. Microscopic images of human feces contain numerous items, such as undigested food, epithelium, bacteria and other formed elements, leading to a complex image composition. Consequently, traditional image quality assessment (IQA) methods cannot accurately assess the quality of fecal microscopic images or even identify the clearest image in the autofocus process. In response to this difficulty, we propos

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Rohovyi, Yu Ye, O. V. Bilookyi, O. H. Ushenko, V. V. Bilookyi, and S. B. Semenenko. "The role of histohematologic barriers and the possibility of using polarization biomedical optics methods in the diagnosis of autoimmune thyroiditis." INTERNATIONAL JOURNAL OF ENDOCRINOLOGY (Ukraine) 20, no. 6 (2024): 452–58. http://dx.doi.org/10.22141/2224-0721.20.6.2024.1442.

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Background. Violation of the integrity of the histohematologic barriers (blood-brain, blood-testis, blood-ocular, blood-labyrinth, blood-thyroid) leads to autoimmune damage to these organs. One of the manifestations of the latter is autoimmune thyroiditis, the structural and quantitative changes of which can be more informatively accurately assessed by polarization biomedical optics. The purpose of the study was to substantiate the possibility of using polarization biomedical optics methods in the diagnosis of autoimmune thyroiditis based on the use of pathophysiological analysis of blood-brai

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Karunamurthy, Dr A., and E. Mohanapriya. "Parascan AI Prediction." International Journal of Research and Review 12, no. 6 (2025): 144–53. https://doi.org/10.52403/ijrr.20250618.

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Malaria continues to be a major global health challenge, particularly in tropical and subtropical regions. Early and accurate detection of malaria is critical for effective treatment and disease control. Traditional diagnosis through microscopic examination of blood smears is labor-intensive, time-consuming, and often prone to human error. To address these challenges, this project proposes an automated malaria detection system based on deep learning techniques. A Convolutional Neural Network (CNN) model was developed and trained on a publicly available dataset containing microscopic images of

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Pattanaik, P. A., and Tripti Swarnkar. "Comparative Analysis of Morphological Techniques for Malaria Detection." International Journal of Healthcare Information Systems and Informatics 13, no. 4 (2018): 49–65. http://dx.doi.org/10.4018/ijhisi.2018100104.

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The genus Plasmodium parasite causes malaria infection. Fast detection and accurate diagnosis of infected and non-infected malaria erythrocytes from microscopic blood smear images open the door to effective assistance and patient-specific treatment. This article presents a comparative experimental analysis of visual detection of infected erythrocytes malaria parasites via the most efficient morphological techniques from gold standard blood smear images. In this article, twelve different widely-used morphological algorithms are evaluated followed by a random forest classifier for detecting infe

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Lin, Sheng Fuu, Chien Hao Tseng, and Chung I. Huang. "Supervised Neural Networks for the Automatic Classification of Leukocytes in Blood Microscope Images." Applied Mechanics and Materials 479-480 (December 2013): 491–95. http://dx.doi.org/10.4028/www.scientific.net/amm.479-480.491.

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In this paper, the application of the supervised learning system to automatic classification of leukocytes processing for the microscopic images analysis is presented. The traditional pattern classification in cellular images is typically made by experienced operators. Such procedures may present a non-standard and unstable accuracy when it depends on the operator’s capabilities and tiredness. In this study, we propose the supervised learning system to achieve an automated segmentation and classification of leukocytes based on supervised neural networks and image processing methods. The experi

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P, Mrs Gokila. "AI DRIVEN BLOOD CELL ANALYSIS FOR MALARIA AND BLOOD CANCER SCREENING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33052.

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Malaria and blood Cancer is one of the deadliest diseases cross the globe. This is caused by the bite of female Anopheles mosquito that transmits the Plasmodium parasites. Some current malaria detection techniques include manual microscopic examination and RDT. These approaches are vulnerable to human mistakes. Early detection of malaria can help in reducing the death rates across the globe.Deep Learning can emerge as a highly beneficial solution in the diagnosis of disease. This model gives a faster and cheaper method for detecting plasmodium parasites. It Was Designed to Identify Malaria Par

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Sazak, Halenur, and Muhammed Kotan. "Automated Blood Cell Detection and Classification in Microscopic Images Using YOLOv11 and Optimized Weights." Diagnostics 15, no. 1 (2024): 22. https://doi.org/10.3390/diagnostics15010022.

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Background/Objectives: Accurate detection and classification of blood cell types in microscopic images are crucial for diagnosing various hematological conditions. This study aims to develop and evaluate advanced architectures for automating blood cell detection and classification using the newly proposed YOLOv10 and YOLOv11 models, with a specific focus on identifying red blood cells (RBCs), white blood cells (WBCs), and platelets in microscopic images as a preliminary step of the complete blood count (CBC). Methods: The Blood Cell Count Detection (BCCD) dataset was enriched using data augmen

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Rohovyi, Yu Ye, O. V. Bilookyi, O. H. Ushenko, and V. V. Bilookyi. "Pathophysiology of tumor progression and possibilities of using polarization biomedical optics methods in the diagnosis of papillary thyroid cancer." INTERNATIONAL JOURNAL OF ENDOCRINOLOGY (Ukraine) 20, no. 8 (2025): 600–606. https://doi.org/10.22141/2224-0721.20.8.2024.1467.

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Background. Clinical diagnosis of papillary thyroid cancer usually occurs at the stage of tumor progression accompanied by intensive processes of growth, invasion, formation of blood vessels to provide blood supply to the tumor, the structure and quantitative changes of which can be more informatively accurately assessed by polarization biomedical optics. The purpose of the study was to substantiate the possibility of using polarization biomedical optics methods in the diagnosis of papillary thyroid cancer based on the principles of comprehensiveness and integrated pathophysiology. Materials a

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Sharma, Anuj, Deepak Prashar, Arfat Ahmad Khan, Faizan Ahmed Khan, and Settawit Poochaya. "Automatic Leukaemia Segmentation Approach for Blood Cancer Classification Using Microscopic Images." Computers, Materials & Continua 73, no. 2 (2022): 3629–48. http://dx.doi.org/10.32604/cmc.2022.030879.

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Devi, Salam Shuleenda, Joyeeta Singha, Manish Sharma, and Rabul Hussain Laskar. "Erythrocyte segmentation for quantification in microscopic images of thin blood smears." Journal of Intelligent & Fuzzy Systems 32, no. 4 (2017): 2847–56. http://dx.doi.org/10.3233/jifs-169227.

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D. G. Carvalho, Isabela, Percy Nohama, and Guilherme N. Nogueira. "General Super-Resolution Techniques." International Journal of Online and Biomedical Engineering (iJOE) 20, no. 14 (2024): 160–76. http://dx.doi.org/10.3991/ijoe.v20i14.50107.

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Super-resolution (SR) is a technique aimed at improving the resolution of images. In blood cell imaging, it aids in the accurate identification and classification of cells. Improving the analysis process of microscopic images is necessary to achieve better disease diagnoses, especially the image quality, so that health professionals can reach a diagnosis closer to the ideal. For those aiming to implement SR algorithms to analyze microscopic blood cell images, it is crucial to determine which algorithms are in use, their intended purposes, future trends, and current gaps. No review of SR techni

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Rohovyi, Y., O. Bilookyi, O. Ushenko, and V. Bilookyi. "Polarization biomedical optics methods in the diagnosis of papillary thyroid carcinoma." Clinical Endocrinology and Endocrine Surgery, no. 4 (December 31, 2024): 93. https://doi.org/10.30978/cees-2024-4-93.

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Clinical diagnosis of papillary thyroid carcinoma (PTC) usually occurs at the stage of tumor progression, accompanied by intensive processes of growth, invasion, formation of blood vessels to provide blood supply to the tumor, the structure and quantitative changes of which can be more informatively accurately assessed by polarization biomedical optics. Objective — to substantiate the possibility of using polarization biomedical optics methods in the diagnosis of PTC based on the principles of integrative and integrated pathophysiology. Materials and methods. Two groups of patients were studie

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Hazra, Debapriya, Yung-Cheol Byun, Woo Kim, and Chul-Ung Kang. "Synthesis of Microscopic Cell Images Obtained from Bone Marrow Aspirate Smears through Generative Adversarial Networks." Biology 11, no. 2 (2022): 276. http://dx.doi.org/10.3390/biology11020276.

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Every year approximately 1.24 million people are diagnosed with blood cancer. While the rate increases each year, the availability of data for each kind of blood cancer remains scarce. It is essential to produce enough data for each blood cell type obtained from bone marrow aspirate smears to diagnose rare types of cancer. Generating data would help easy and quick diagnosis, which are the most critical factors in cancer. Generative adversarial networks (GAN) are the latest emerging framework for generating synthetic images and time-series data. This paper takes microscopic cell images, preproc

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Khan Tusar, Md Taufiqul Haque, Md Touhidul Islam, Abul Hasnat Sakil, M. N. Huda Nahid Khandaker, and Md Monir Hossain. "An Intelligent Telediagnosis of Acute Lymphoblastic Leukemia using Histopathological Deep Learning." Journal of Computing Theories and Applications 2, no. 1 (2024): 1–12. http://dx.doi.org/10.62411/jcta.10358.

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Leukemia, a global health challenge characterized by malignant blood cell proliferation, demands innovative diagnostic techniques due to its increasing incidence. Among leukemia types, Acute Lymphoblastic Leukemia (ALL) emerges as a particularly aggressive form affecting diverse age groups. This study proposes an advanced mechanized system utilizing Deep Neural Networks for detecting ALL blast cells in microscopic blood smear images. Achieving a remarkable accuracy of 97% using MobileNetV2, our system demonstrates high sensitivity and specificity in identifying multiple ALL sub-types. Furtherm

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Honest, Wapalila, Kaijage Shubi, and Leo Judith. "Identifying Requirements for Enhanced Deep Learning Classification in Malaria Microscopic Images Analysis." Indian Journal of Science and Technology 17, no. 42 (2024): 4372–79. https://doi.org/10.17485/IJST/v17i42.2727.

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Abstract <strong>Objective:</strong> The major goal of the study is to improve malaria diagnosis by applying advanced deep learning models, improving the model architecture, increasing dataset quality, and complex image preprocessing methodologies for more correct blood smear categorization. <strong>Methods:</strong> Blood smear images, covering both malaria-positive and negative microscopic images, were aggregated and pre-processed to assure dataset quality, employing techniques such as image standardization, noise reduction, and contrast enhancement. The study has analyzed the

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Baig, Raheel, Abdur Rehman, Abdullah Almuhaimeed, Abdulkareem Alzahrani, and Hafiz Tayyab Rauf. "Detecting Malignant Leukemia Cells Using Microscopic Blood Smear Images: A Deep Learning Approach." Applied Sciences 12, no. 13 (2022): 6317. http://dx.doi.org/10.3390/app12136317.

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Leukemia is a form of blood cancer that develops when the human body’s bone marrow contains too many white blood cells. This medical condition affects adults and is considered a prevalent form of cancer in children. Treatment for leukaemia is determined by the type and the extent to which cancer has developed across the body. It is crucial to diagnose leukaemia early in order to provide adequate care and to cure patients. Researchers have been working on advanced diagnostics systems based on Machine Learning (ML) approaches to diagnose leukaemia early. In this research, we employ deep learning

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Jeganathan, Balachandar. "Automated Plasma Cell Segmentation for Multiple Myeloma Diagnosis: A Deep Learning Approach Using a Novel Dataset." International Journal of Research and Innovation in Applied Science X, no. II (2025): 366–75. https://doi.org/10.51584/ijrias.2025.10020032.

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The development of computer-assisted diagnostic tools for cancer detection has gained significant momentum, with image processing playing a pivotal role in automating the analysis of microscopic images. This work focuses on Multiple Myeloma (MM), a type of blood cancer affecting plasma cells, and addresses the critical challenge of plasma cell segmentation in microscopic images. Accurate segmentation is essential for quantifying malignant versus healthy cells, a key step in MM diagnosis and treatment planning. Plasma cell segmentation is inherently challenging due to the variability in the siz

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M, Ankitha. "Deep Learning and Genetic Disorder Detection: A Dual Approach to Detect Sickle Cell and Cystic Fibrosis." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 1307–11. https://doi.org/10.22214/ijraset.2025.72372.

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Automated screening of genetic blood disorders like Sickle Cell Disease (SCD) and Cystic Fibrosis (CF) can greatly augment screening in low-resource environments. We present a hybrid deep-learning architecture of classification (CNN) and object detection (YOLOv3) to screen microscopic images and medical scans to detect these diseases. The pipeline utilizes preprocessed, labeled blood-smear images to detect abnormal erythrocytes and classify cell morphology. We further incorporate hybrid classifiers (Random Forest, SVM, Deep Neural Networks) on convolutional features to enhance accuracy. Using

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Boit, Sorio, and Rajvardhan Patil. "An Efficient Deep Learning Approach for Malaria Parasite Detection in Microscopic Images." Diagnostics 14, no. 23 (2024): 2738. https://doi.org/10.3390/diagnostics14232738.

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Background: Malaria is a life-threatening disease spread by infected mosquitoes, affecting both humans and animals. Its symptoms range from mild to severe, including fever, muscle discomfort, coma, and kidney failure. Accurate diagnosis is crucial but challenging, relying on expert technicians to examine blood smears under a microscope. Conventional methods are inefficient, while machine learning approaches struggle with complex tasks and require extensive feature engineering. Deep learning, however, excels in complex tasks and automatic feature extraction. Objective: This paper presents EDRI,

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Abbas, Naveed, Zulkifli Mohamad, Hanan Abdullah, and Ayman Altameem. "Clustered Red Blood Cells Splitting via Boundary Analysis in Microscopic Thin Blood Smear Digital Images." International Journal of Technology 6, no. 3 (2015): 306. http://dx.doi.org/10.14716/ijtech.v6i3.522.

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Sadeghian, Farnoosh, Zainina Seman, Abdul Rahman Ramli, Badrul Hisham Abdul Kahar, and M.-Iqbal Saripan. "A Framework for White Blood Cell Segmentation in Microscopic Blood Images Using Digital Image Processing." Biological Procedures Online 11, no. 1 (2009): 196–206. http://dx.doi.org/10.1007/s12575-009-9011-2.

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Wu, Chuanchao, Zhibin Wang, Peng Xue, and Wenyan Liu. "Boundary Segmentation of Vascular Images in Fourier Domain Doppler Optical Coherence Tomography Based on Deep Learning." Electronics 13, no. 13 (2024): 2516. http://dx.doi.org/10.3390/electronics13132516.

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Microscopic and ultramicroscopic vascular sutures are indispensable in surgical procedures such as arm transplantation and finger reattachment. The state of the blood vessels after suturing, which may feature vascular patency, narrowness, and blockage, determines the success rate of the operation. If we can take advantage of the golden window of opportunity after blood vessel suture and before muscle tissue suture to achieve an accurate and objective assessment of blood vessel status, this will not only reduce medical costs but will also offer social benefits. Doppler optical coherence tomogra

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Sbrollini, Agnese, Selene Tomassini, Ruba Sharaan, Micaela Morettini, Aldo Franco Dragoni, and Laura Burattini. "Leukocyte classification for acute lymphoblastic leukemia timely diagnosis by interpretable artificial neural network." Journal of Autonomous Intelligence 6, no. 1 (2023): 594. http://dx.doi.org/10.32629/jai.v6i1.594.

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Leukemia is a blood cancer characterized by leukocyte overproduction. Clinically, the reference for acute lymphoblastic leukemia diagnosis is a blood biopsy that allows obtain microscopic images of leukocytes, whose early-stage classification into leukemic (LEU) and healthy (HEA) may be disease predictor. Thus, the aim of this study is to propose an interpretable artificial neural network (ANN) for leukocyte classification to timely diagnose acute lymphoblastic leukemia. The “ALL_IDB2” dataset was used. It contains 260 microscopic images showing leukocytes acquired from 130 LEU and 130 HEA sub

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Somasekar, J., Y. C. A. Padmanabha Reddy, and G. Ramesh. "Border Detection of Malaria Infected Cells in Microscopic Images for Diagnosis: A Computer Vision Approach." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 4643–47. http://dx.doi.org/10.1166/jctn.2020.9292.

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A computer vision approach is presented for border detection of malaria infected cells in microscopic blood images for accurate diagnosis. First, the microscopic 24-bits RGB color blood image converted in to 8-bits gray scale image for a single channel procesing. The poroposed two-stage thresholdingmethod used for segmentation of malaria infected cells. Regarding border irregularities, the chosen descriptor is the perimeter factor and 4-connected neighbourhood. The experimental results on benchmark dataset that comprises around 300 images show that the proposed method successfully detects bord

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Shahzad, Muhammad, Arif Iqbal Umar, Muazzam A. Khan, Syed Hamad Shirazi, Zakir Khan, and Waqas Yousaf. "Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Images." Computational and Mathematical Methods in Medicine 2020 (January 21, 2020): 1–13. http://dx.doi.org/10.1155/2020/4015323.

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Previous works on segmentation of SEM (scanning electron microscope) blood cell image ignore the semantic segmentation approach of whole-slide blood cell segmentation. In the proposed work, we address the problem of whole-slide blood cell segmentation using the semantic segmentation approach. We design a novel convolutional encoder-decoder framework along with VGG-16 as the pixel-level feature extraction model. The proposed framework comprises 3 main steps: First, all the original images along with manually generated ground truth masks of each blood cell type are passed through the preprocessi

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P, Aiswariya. "A Survey on Feature Extraction for Leukemia Detection in Blood Microscopic Images." Revista Gestão Inovação e Tecnologias 11, no. 4 (2021): 3477–84. http://dx.doi.org/10.47059/revistageintec.v11i4.2386.

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A. Bhavnani, Lata, Udesang K. Jaliya, and Mahasweta J. Joshi. "Segmentation and Counting of WBCs and RBCs from Microscopic Blood Sample Images." International Journal of Image, Graphics and Signal Processing 8, no. 11 (2016): 32–40. http://dx.doi.org/10.5815/ijigsp.2015.11.05.

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Bhavnani, Lata A., Udesang K. Jaliya, and Mahasweta J. Joshi. "Segmentation and Counting of WBCs and RBCs from Microscopic Blood Sample Images." International Journal of Image, Graphics and Signal Processing 8, no. 11 (2016): 32–40. http://dx.doi.org/10.5815/ijigsp.2016.11.05.

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