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Segmentation and Classification of Leukocytes Using Neural Networks: A Generalization Direction [chapter]

Pedro Rodrigues, Manuel Ferreira, João Monteiro
2008 Studies in Computational Intelligence  
The segmentation and classification of leukocytes is an application where this fact is evident.  ...  One is an unsupervised network and the other one is a supervised neural network. The goal is to achieve a better generalizing system while still doing well the role of a specialistic system.  ...  The presentation of our solutions is done in two parts. The first one is about a method that uses a supervised neural network to produce the segmentation and the classification of leukocytes.  ... 
doi:10.1007/978-3-540-75398-8_17 fatcat:pfaqzmyzqvabzlun6zruhhzxv4

Convolution Neural Network Models for Acute Leukemia Diagnosis

Maila Claro, Luis Vogado, Rodrigo Veras, Andre Santana, Joao Tavares, Justino Santos, Vinicius Machado
2020 2020 International Conference on Systems, Signals and Image Processing (IWSSIP)  
This work presents a convolutional neural networks (CNNs) architecture capable of differentiating blood slides with ALL, AML and Healthy Blood Slides (HBS).  ...  The experiments were performed using 16 datasets with 2,415 images, and the accuracy of 97.18% and a precision of 97.23% were achieved.  ...  We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used in this research.  ... 
doi:10.1109/iwssip48289.2020.9145406 dblp:conf/iwssip/ClaroVVS0SM20 fatcat:twulqffbw5evtn2ov5yyr7p3gy

GPU implementation of spiking neural networks for color image segmentation

Ermai Xie, Martin McGinnity, QingXiang Wu, Jianyong Cai, Rontai Cai
2011 2011 4th International Congress on Image and Signal Processing  
However, it is time-consuming to simulate a large scale of spiking neurons in the networks using CPU programming.  ...  Spiking neural networks (SNN) are powerful computational model inspired by the human neural system for engineers and neuroscientists to simulate intelligent computation of the brain.  ...  CONCLUSION AND FURTHER WORK This paper presents a general implementation of spiking neural networks using CUDA architecture in the GPU.  ... 
doi:10.1109/cisp.2011.6100451 fatcat:fyxbvw7uxzbijejmufnfza2xcu

BCNet: A Novel Network for Blood Cell Classification

Ziquan Zhu, Siyuan Lu, Shui-Hua Wang, Juan Manuel Górriz, Yu-Dong Zhang
2022 Frontiers in Cell and Developmental Biology  
Therefore, each deep convolution neural network needs a lot of time to get the results.  ...  The final outputs of our BCNet are generated by the ensemble of the predictions from the three randomized neural networks by the majority voting.  ...  Marostica et al. (2021) suggested using a deep convolutional neural network for the detection and diagnosis of renal cancer.  ... 
doi:10.3389/fcell.2021.813996 pmid:35047515 pmcid:PMC8762289 fatcat:jfx7qh5ekbbw3hizksqreeu65i

Classification of Leukemia and Leukemoid Using VGG-16 Convolutional Neural Network Architecture

G. Sriram, T. R. Ganesh Babu, R. Praveena, J. V. Anand
2022 MCB Molecular and Cellular Biomechanics  
VGG16 (Visual Geometric Group) CNN (Convolution Neural Network) architecture based deep learning technique is being incorporated for classification and counting WBC type from segmented images.  ...  The VGG16 architecture based CNN used for classification and segmented images obtained from first part were tested to identify WBC blasts.  ...  A transfer learning of the pre-trained neural network using VGG-19 convolutional neural network (CNN) with an adapted connected classifier was used [12] and Gray Level Co-occurrence Matrix (GLCM) [13  ... 
doi:10.32604/mcb.2022.016966 fatcat:mcg5i2yqgvgj3cq6uadqt2l5v4

Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set

Christian Matek, Sebastian Krappe, Christian Münzenmayer, Torsten Haferlach, Carsten Marr
2021 Blood  
It allows us to train high-quality classifiers of leukocyte cytomorphology that identify a wide range of diagnostically relevant cell species with high precision and recall.  ...  We applied convolutional neural networks (CNNs) to a large data set of 171 374 microscopic cytological images taken from BM smears from 945 patients diagnosed with a variety of hematological diseases.  ...  Classification analysis and explainability Because they are developed based on the training set in a datadriven way, the classification decisions of neural networks do not lend themselves to direct human  ... 
doi:10.1182/blood.2020010568 pmid:34792573 pmcid:PMC8602932 fatcat:ozfcjkwsbrgo3lxaz7enrrkyiu

Automatic classification of cells in microscopic fecal images using convolutional neural networks

Xiaohui Du, Lin Liu, Xiangzhou Wang, Guangming Ni, Jing Zhang, Ruqian Hao, Juanxiu Lin, Yong Liu
2019 Bioscience Reports  
As for the candidate recognition, we proposed a new convolutional neural network (CNN) architecture based on Inception-v3 and principal component analysis (PCA).  ...  In the process of segmentation, we proposed a morphology extraction algorithm in a complex background.  ...  The second was the feature extractor, with a deep convolutional neural network for each candidate and classification.  ... 
doi:10.1042/bsr20182100 pmid:30872411 pmcid:PMC6449518 fatcat:7ssi4hrj4nai5ereejq6hwrcxa

Convolutional Neural Networks for Recognition of Lymphoblast Cell Images

Tatdow Pansombut, Siripen Wikaisuksakul, Kittiya Khongkraphan, Aniruth Phon-on
2019 Computational Intelligence and Neuroscience  
With deep learning approach, handcrafted feature engineering can be eliminated because a deep learning method can automate this task through the multilayer architecture of a convolutional neural network  ...  In this work, we implement a CNN classifier to explore the feasibility of deep learning approach to identify lymphocytes and ALL subtypes, and this approach is benchmarked against a dominant approach of  ...  Classification of ALL Using ConVNet Convolutional Neural Networks.  ... 
doi:10.1155/2019/7519603 pmid:31281337 pmcid:PMC6589284 fatcat:odgdhe7v45b55hbvm5nlgwkp54

An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification

Muhammad Zakir Ullah, Yuanjie Zheng, Jingqi Song, Sehrish Aslam, Chenxi Xu, Gogo Dauda Kiazolu, Liping Wang
2021 Applied Sciences  
This work presents a non-invasive, convolutional neural network (CNN) based approach that utilizes medical images to perform the diagnosis task.  ...  Leukemia is a kind of blood cancer that influences people of all ages and is one of the leading causes of death worldwide.  ...  Furthermore, they used transfer learning or fine-tuning of deep neural networks for ALL classification.  ... 
doi:10.3390/app112210662 fatcat:fvju5xhatfdgrpa7foyknfmiwa


2008 International Journal of Neural Systems  
The performances of the EmNN and a conventional BP-based neural network, using two topologies for each network, will be compared when applied to a blood cell type identification problem.  ...  This paper presents an emotional neural network (EmNN) that is based on the emotional back propagation (EmBP) learning algorithm.  ...  The remaining 300 blood cell images are not exposed to the neural networks during training, and are used to test/generalize the trained networks.  ... 
doi:10.1142/s0129065708001713 pmid:18991367 fatcat:j25j5xqrjnc6taqbfsngukzlvm

WBC Analysis using Data Augment Method and Convolutional Neural Network

In our paper, we are concentrating on collecting the WBC count using the Data augmentation method and Convolutional Neural Network.The Quality of image is improved in comparison with number of augmented  ...  RBC is otherwise known as Erythrocyte and it does not have a nucleus, with pigment hemoglobin. Due to the presence of this pigment, blood is red in color.  ...  Simple Neural Network is built to classify into twelve classes using DL Studio.  ... 
doi:10.35940/ijitee.b7632.129219 fatcat:6pbzo5ms4jhgle5urzepelglwy

Parallel Capsule Networks for Classification of White Blood Cells [article]

Juan P. Vigueras-Guillén, Arijit Patra, Ola Engkvist, Frank Seeliger
2021 arXiv   pre-print
Capsule Networks (CapsNets) is a machine learning architecture proposed to overcome some of the shortcomings of convolutional neural networks (CNNs).  ...  In this work, we present a new architecture, parallel CapsNets, which exploits the concept of branching the network to isolate certain capsules, allowing each branch to identify different entities.  ...  Capsule Networks (CapsNets) [6, 19] is a new neural network architecture that tackles those shortcomings by using capsules.  ... 
arXiv:2108.02644v2 fatcat:366sevjrgjbonhl5djyilk2tgu

Tens of images can suffice to train neural networks for malignant leukocyte detection

Jens P. E. Schouten, Christian Matek, Luuk F. P. Jacobs, Michèle C. Buck, Dragan Bošnački, Carsten Marr
2021 Scientific Reports  
AbstractConvolutional neural networks (CNNs) excel as powerful tools for biomedical image classification. It is commonly assumed that training CNNs requires large amounts of annotated data.  ...  Saliency maps and layer-wise relevance propagation visualizations suggest that the network learns to increasingly focus on nuclear structures of leukocytes as the number of training images is increased  ...  This work was supported by the German Science Foundation DFG within the Collaborative Research Center SFB 1243 "Cancer Evolution" with a research grant for MB.  ... 
doi:10.1038/s41598-021-86995-5 pmid:33846442 fatcat:kniztr57zbbhvfuyhmqdmlwpeu

A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images

Siraj Khan, Muhammad Sajjad, Tanveer Hussain, Amin Ullah, Ali Shariq Imran
2020 IEEE Access  
This review provides an in-depth analysis of available TML and DL techniques for MIA with a significant focus on leukocytes classification in blood smear images and other medical imaging domains, i.e.,  ...  These methods substantially improved the diagnoses of automatic brain tumor and leukemia/blood cancer detection and can assist the hematologist and doctors by providing a second opinion.  ...  LEUKOCYTES CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS (CNN) CNN consists of multiple convolutional, pooling, and fully interconnected layers with activation functions.  ... 
doi:10.1109/access.2020.3048172 fatcat:gyyzj4dq7vh7lgwhzkf2lr6ld4

Automated red blood cells extraction from holographic images using fully convolutional neural networks

Faliu Yi, Inkyu Moon, Bahram Javidi
2017 Biomedical Optics Express  
In this paper, we present two models for automatically extracting red blood cells (RBCs) from RBCs holographic images based on a deep learning fully convolutional neural network (FCN) algorithm.  ...  Then, some RBCs phase images are manually segmented and used as training data to fine-tune the FCN.  ...  [35] used convolutional neural networks for image classification to very good effect. Mikolov et al. [36] and Liu et al.  ... 
doi:10.1364/boe.8.004466 pmid:29082078 pmcid:PMC5654793 fatcat:k2wwdfoazrflrjfex2evpo3ziq
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