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Pathological Analysis of Blood Cells Using Deep Learning Techniques

Virender Ranga, Shivam Gupta, Priyansh Agrawal, Jyoti Meena
2020 Recent Advances in Computer Science and Communications  
This enables the area of deep learning and machine learning to deep dive into this field of medical sciences.  ...  In the present study, a neural based network has been proposed for classification of blood cells images into various categories.  ...  The database had been provided to help in comparative evaluation of various techniques for classification of white blood cells.  ... 
doi:10.2174/2666255813999200904113251 fatcat:xffdyrjljvbo3gusuzaa3cahea

Analysis of White Blood Cell Segmentation Techniques and Classification Using Deep Convolutional Neural Network for Leukemia Detection

Snehal Laddha
2018 Helix  
In this study, we have performed comparative analysis of white blood cells segmentation techniques and evaluated the performance of pretrained deep CNN with multiclass models for Support Vector Machine  ...  Then cells are detected and boundaries are traced using segmentation techniques for morphological analysis. Typical image processing steps for WBC segmentation are as shown in figure 2.  ...  Acknowledgement: We extend our sincere thanks to Fabio Scotti, Department of Information Technology, and University of Milan, Italy for making dataset ALL-IDB of Peripheral Blood samples of normal individuals  ... 
doi:10.29042/2018-4519-4524 fatcat:m7bw3unqhrfkfmql5sgiro2lxi

A Survey On Image Processing Techniques And Deep Learning Algorithm For Blood Cell Classification

Saranya Vijayan, Dr. Radha Venkatachalam
2021 Zenodo  
In this paper literature survey of some recent papers on blood cell classification using image processing techniques and deep learning algorithms have been reviewed.  ...  This paper aims to analyze the existing image processing techniques for leukemic blood cell classification. Keywords: blood diseases, leukemia, deep learning algorithm.  ...  In this paper literature survey of some recent papers on blood cell classification using image processing techniques and deep learning algorithms have been reviewed.  ... 
doi:10.5281/zenodo.4533697 fatcat:ojvu22fiyvgojpa3dc67vyvd3a

Induced Pluripotent Stem Cell-Based Drug Screening by Use of Artificial Intelligence

Dai Kusumoto, Shinsuke Yuasa, Keiichi Fukuda
2022 Pharmaceuticals  
treatments because differentiated cells from iPSCs can recapitulate the cellular pathology of patients with genetic mutations.  ...  Recently, the accuracy of image analysis has dramatically improved with the development of artificial intelligence (AI) technology.  ...  Acknowledgments: We thank all of our laboratory members for their assistance. Conflicts of Interest: The authors declare no conflict of interest. Pharmaceuticals 2022, 15, 562  ... 
doi:10.3390/ph15050562 pmid:35631387 pmcid:PMC9145330 fatcat:7jwjyarjp5egnauwcdni4kpkpa

Systematic Review of the State of the Art Regarding the Identification of Cancer Cells of the Leukemia Type with Digital Image Processing

José de Jesús Moya, Manuel Martin
2020 Research on computing science  
The present diagnosis is based on a review of the literature of several research projects carried out on the use of tools in the detection of leukemia cancer.  ...  This paper presents a discussion of the existing literature to know the current state of knowledge about the identification of leukemia using digital images and areas of opportunity for future work are  ...  The challenges facing the use of machine learning, as well as deep learning algorithms in the pathology, are diverse, from the digitalization of cell samples, manual labeling in case of supervised learning  ... 
dblp:journals/rcs/MoyaM20 fatcat:2dhi7kjhn5f57jhgael3hth4je

Deep CNNs for Peripheral Blood Cell Classification [article]

Ekta Gavas, Kaustubh Olpadkar
2021 arXiv   pre-print
Our work provides empirical baselines and benchmarks on standard deep-learning architectures for microscopic peripheral blood cell recognition task.  ...  The dataset is publicly available, with large number of normal peripheral blood cells acquired using the CellaVision DM96 analyzer and identified by expert pathologists into eight different cell types.  ...  Explore and benchmark 27 standard deep CNN architectures for blood cell classification using transfer learning. 3.  ... 
arXiv:2110.09508v1 fatcat:u7xsgbefqbfshlzmn6jcusegvy

Machine Learning Methods for Histopathological Image Analysis

Daisuke Komura, Shumpei Ishikawa
2018 Computational and Structural Biotechnology Journal  
Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques.  ...  In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions  ...  Therefore, quantitative analysis of tumor infiltrating immune cells in slides using machine learning techniques will be one of the emerging themes in digital histopathological image analysis.  ... 
doi:10.1016/j.csbj.2018.01.001 pmid:30275936 pmcid:PMC6158771 fatcat:lei72yiayzclfgmiaiullzdkt4

Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology

Hanadi El Achi, Joseph D Khoury
2020 Cancers  
By its nature, digitization of analog histology data renders it amenable to analysis using deep learning/artificial intelligence (DL/AI) techniques.  ...  Digital Pathology is the process of converting histology glass slides to digital images using sophisticated computerized technology to facilitate acquisition, evaluation, storage, and portability of histologic  ...  histologic information using machine learning (ML) techniques [1] .  ... 
doi:10.3390/cancers12040797 pmid:32224980 pmcid:PMC7226574 fatcat:4tw7ws7ysncypgabwjvipgmdmi

Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis [article]

Florian A. Hölzl, Daniel Rueckert, Georgios Kaissis
2022 arXiv   pre-print
In this work, we propose to use steerable equivariant convolutional networks for medical image analysis with DP.  ...  Machine learning with formal privacy-preserving techniques like Differential Privacy (DP) allows one to derive valuable insights from sensitive medical imaging data while promising to protect patient privacy  ...  Thus, advanced deep learning techniques, able to learn robust and generalizable features under DP must be developed (Tramèr and Boneh, 2020) .  ... 
arXiv:2209.04338v1 fatcat:ewnriypkbjgynhfsqiip5wdtua

Towards the Segmentation and Classification of White Blood Cell Cancer Using Hybrid Mask-Recurrent Neural Network and Transfer Learning

Sumit Kumar Das, Kazi Soumik Islam, Tanzila Ahsan Neha, Mohammad Monirujjaman Khan, Sami Bourouis, Yuvaraja Teekaraman
2021 Contrast Media & Molecular Imaging  
Inside the bone marrow, plasma cells are created, and they are a type of white blood cells. They are made from B lymphocytes.  ...  The nobility of this research is that it provides a computer-assisted technique for recognizing and detecting myeloma cells in bone marrow smears.  ...  [11] discussed medical image analysis using deep learning, and it is said that deep learning is a machine learning technique. e possibility of using deep learning in medical image analysis in machine  ... 
doi:10.1155/2021/4954854 pmid:34955694 pmcid:PMC8660215 fatcat:2fnghtiwmrehldambhjs6ifmyi

Biobanking in the digital pathology era

2021 Oncology Research  
diagnosis and biomarkers analysis.  ...  Digital Pathology is becoming more and more important to achieve the goal of precision medicine.  ...  Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.  ... 
doi:10.32604/or.2022.024892 fatcat:pbg6l263ubbpzezykrq474rdpy

A Proposed Model to eliminate the Confusion of Hematological Diseases in Thin Blood Smear by Using Deep Learning-Pretrained Model

Mahir M. Sharif, Hassan Abdelrhman Mohammed, Eltahir Mohmmed Hussein
2022 Omdurman Islamic University Journal  
The techniques in deep learning have been implemented where the CNN (Alexnet and Resnet50) image recognition model was applied to detect patterns and extract features of the different types of malaria  ...  This research aimed to developing and designing a model for resolving the confusion between hematology in a thin blood smear by means of a pre-defined deep learning model for detection and identification  ...  Others in [8] proposes to develop a deep learning model to discuss the blood cell analysis problem, which is one of the several challenging problems in blood investigation.  ... 
doi:10.52981/oiuj.v18i1.2038 fatcat:6jyhubntxvevlnie7qwh3vdpim

Advances in Imaging Modalities, Artificial Intelligence, and Single Cell Biomarker Analysis, and Their Applications in Cytopathology

Ryan P. Lau, Teresa H. Kim, Jianyu Rao
2021 Frontiers in Medicine  
Many new automated multiplex modalities such as antibody barcoding with cleavable DNA (ABCD), single cell analysis for tumor phenotyping (SCANT), fast analytical screening technique fine needle aspiration  ...  Imaging technologies such as optical coherence tomography, optical projection tomography, and quantitative phase microscopy allow analysis of tissues and cells in 3-dimensions and with subcellular granularity  ...  Advances in AI, image analysis, and deep learning are augmenting the myriad ways that computational pathology can be applied to cytopathology.  ... 
doi:10.3389/fmed.2021.689954 fatcat:uxog2khm4fgchpssk6y66pndaq

Next-Generation Digital Histopathology of the Tumor Microenvironment

Felicitas Mungenast, Achala Fernando, Robert Nica, Bogdan Boghiu, Bianca Lungu, Jyotsna Batra, Rupert C. Ecker
2021 Genes  
image cytometry and advanced methods for machine and deep learning.  ...  The need for quantitative analyses has led to a renaissance of optical instruments and imaging techniques.  ...  Further studies that used deep convolutional/deep learning/machine learning networks for cancer tissue classification/TME on HE cancer samples for follow-up alignment with clinicopathological parameters  ... 
doi:10.3390/genes12040538 pmid:33917241 fatcat:4szq73dpozdgpm7yrucs265z3y

Comprehensive Analysis of State-of-the-Art CAD Tools and Technique for Chronic Kidney Disease (CKD)

2021 International Journal of Big Data and Analytics in Healthcare  
Many researchers have focused their work to identify the kidney disease or classify the kidney disease using computational technology because of the mortality rate is very high in kidney patients.  ...  Primary focus of this paper is review the current research work based on computational advancement in the area of kidney disease and also identify the gaps or future scope to identify and predict the kidney  ...  traditional texture analysis methods using deep learning and HOG -GBM features. 70 samples of 2-D foetal ultrasound images used to check accuracy of proposed technique.  ... 
doi:10.4018/ijbdah.287605 fatcat:dowwf2blkjc7hjjnuq5auu7lmm
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