scholarly journals White Blood Cells (WBC) Images Classification Using CNN-Based Networks

2021 ◽  
Author(s):  
Changhun Jung ◽  
Mohammed Abuhamad ◽  
David Mohaisen ◽  
Kyungja Han ◽  
DaeHun Nyang
Keyword(s):  
Transfer Learning ◽  
Real World ◽  
Blood Cells ◽  
Large Scale ◽  
White Blood Cells ◽  
Research Work ◽  
Class Imbalance ◽  
Learning Context ◽  
Generative Adversarial Network ◽  
Large Scale Dataset

Abstract Background: Computer-aided methods for analyzing white blood cells (WBC) are popular due to the complexity of the manual alternatives. Recent works have shown highly accurate segmentation and detection of white blood cells from microscopic blood images. However, the classification of the observed cells is still a challenge, in part due to the distribution of the five types that affect the condition of the immune system.Methods: (i) This work proposes W-Net, a CNN-based method for WBC classification. We evaluate W-Net on a real-world large-scale dataset that includes 6,562 real images of the five WBC types. (ii) For further benefits, we generate synthetic WBC images using Generative Adversarial Network to be used for education and research purposes through sharing.Results: (i) W-Net achieves an average accuracy of 97%. In comparison to state-of-the-art methods in the field of WBC classification, we show that W-Net outperforms other CNN- and RNN-based model architectures. Moreover, we show the benefits of using pre-trained W-Net in a transfer learning context when fine-tuned to specific task or accommodating another dataset. (ii) The synthetic WBC images are confirmed by experiments and a domain expert to have a high degree of similarity to the original images. The pre-trained W-Net and the generated WBC dataset are available for the community to facilitate reproducibility and follow up research work.Conclusion: This work proposed W-Net, a CNN-based architecture with a small number of layers, to accurately classify the five WBC types. We evaluated W-Net on a real-world large-scale dataset and addressed several challenges such as the transfer learning property and the class imbalance. W-Net achieved an average classification accuracy of 97%. We synthesized a dataset of new WBC image samples using DCGAN, which we released to the public for education and research purposes.

scholarly journals Legal Judgment Prediction Based on Multiclass Information Fusion

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Kongfan Zhu ◽  
Rundong Guo ◽  
Weifeng Hu ◽  
Zeqiang Li ◽  
Yujun Li
Keyword(s):  
Information Fusion ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
External Information ◽  
Criminal Cases ◽  
Law System ◽  
Large Scale Dataset ◽  
Assistant Systems ◽  
Civil Law System

Legal judgment prediction (LJP), as an effective and critical application in legal assistant systems, aims to determine the judgment results according to the information based on the fact determination. In real-world scenarios, to deal with the criminal cases, judges not only take advantage of the fact description, but also consider the external information, such as the basic information of defendant and the court view. However, most existing works take the fact description as the sole input for LJP and ignore the external information. We propose a Transformer-Hierarchical-Attention-Multi-Extra (THME) Network to make full use of the information based on the fact determination. We conduct experiments on a real-world large-scale dataset of criminal cases in the civil law system. Experimental results show that our method outperforms state-of-the-art LJP methods on all judgment prediction tasks.

scholarly journals Prioritizing Causal Risk Factors for Severe COVID-19: An Exhaustive Mendelian Randomization Study

2021 ◽  
Author(s):  
Yitang Sun ◽  
Jingqi Zhou ◽  
Kaixiong Ye
Keyword(s):  
Risk Factors ◽  
Fat Mass ◽  
Blood Cells ◽  
Drug Targets ◽  
Large Scale ◽  
Mendelian Randomization ◽  
White Blood Cells ◽  
Fat Free Mass ◽  
Cell Counts ◽  
Increased Risk

Abstract Identifying causal risk factors for severe coronavirus disease 2019 (COVID-19) is critical for its prevention and treatment. Many associated pre-existing conditions and biomarkers have been reported, but these observational associations suffer from confounding and reverse causation. Here, we perform a large-scale two-sample Mendelian randomization (MR) analysis to evaluate the causal roles of many traits in severe COVID-19. Our results highlight multiple body mass index (BMI)-related traits as risk-increasing: BMI (OR:1.89, 95% CI:1.51–2.37), hip circumference (OR:1.46, 1.15–1.85), and waist circumference (OR:1.82, 1.36–2.43). Our multivariable MR analysis further shows that the BMI-related effect is driven by fat mass (OR:1.63, 1.03–2.58), but not fat-free mass (OR:1.00, 0.61–1.66). Several white blood cell counts are negatively associated with severe COVID-19, including those of neutrophils (OR:0.76, 0.61–0.94), granulocytes (OR:0.75, 0.601–0.93), and myeloid white blood cells (OR:0.77, 0.62–0.96). Furthermore, some circulating proteins are associated with an increased risk of (e.g., zinc-alpha-2-glycoprotein) or protection from severe COVID-19 (e.g., interleukin-3/6 receptor subunit alpha). Our study shows that fat mass and white blood cells underlie the etiology of severe COVID-19. It also identifies risk and protective factors that could serve as drug targets and guide the effective protection of high-risk individuals.

scholarly journals COVID-GAN+: Estimating Human Mobility Responses to COVID-19 through Spatio-temporal Generative Adversarial Networks with Enhanced Features

2022 ◽  
Vol 13 (2) ◽  
pp. 1-23
Author(s):  
Han Bao ◽  
Xun Zhou ◽  
Yiqun Xie ◽  
Yingxue Zhang ◽  
Yanhua Li
Keyword(s):  
Spatial Heterogeneity ◽  
Real World ◽  
Large Scale ◽  
Census Data ◽  
Human Mobility ◽  
Training Data ◽  
Estimation Accuracy ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Spatio Temporal

Estimating human mobility responses to the large-scale spreading of the COVID-19 pandemic is crucial, since its significance guides policymakers to give Non-pharmaceutical Interventions, such as closure or reopening of businesses. It is challenging to model due to complex social contexts and limited training data. Recently, we proposed a conditional generative adversarial network (COVID-GAN) to estimate human mobility response under a set of social and policy conditions integrated from multiple data sources. Although COVID-GAN achieves a good average estimation accuracy under real-world conditions, it produces higher errors in certain regions due to the presence of spatial heterogeneity and outliers. To address these issues, in this article, we extend our prior work by introducing a new spatio-temporal deep generative model, namely, COVID-GAN+. COVID-GAN+ deals with the spatial heterogeneity issue by introducing a new spatial feature layer that utilizes the local Moran statistic to model the spatial heterogeneity strength in the data. In addition, we redesign the training objective to learn the estimated mobility changes from historical average levels to mitigate the effects of spatial outliers. We perform comprehensive evaluations using urban mobility data derived from cell phone records and census data. Results show that COVID-GAN+ can better approximate real-world human mobility responses than prior methods, including COVID-GAN.

scholarly journals Hematological indices and abnormalities among patients with uncomplicated falciparum malaria in Kosti city of the White Nile State, Sudan: A comparative study

2021 ◽  
Author(s):  
Ahmed M. E. Elkhalifa ◽  
Rashad Abdul-Ghani ◽  
Abdelhakam G. Tamomh ◽  
Nur Eldin Eltaher ◽  
Nada Y. Ali ◽  
...  
Keyword(s):  
Platelet Count ◽  
Falciparum Malaria ◽  
Blood Cells ◽  
Large Scale ◽  
White Blood Cells ◽  
Hematological Indices ◽  
Low Level ◽  
White Nile ◽  
Hb Concentration ◽  
Rbc Count

Abstract Background: Hematological abnormalities are common features in falciparum malaria but vary among different populations across countries. Therefore, we compared hematological indices and abnormalities between Plasmodium falciparum-infected patients and malaria-negative subjects in Kosti city of the White Nile State, Sudan. Methods: A comparative, cross-sectional study was conducted at the Medical Technology Laboratory Unit of Kosti Teaching Hospital from June to December 2018. A total of 392 participants (192 P. falciparum-infected patients and 200 malaria-negative subjects) were recruited in the study. Hematological indices of hemoglobin (Hb), red blood cells (RBCs), white blood cells (WBCs) and platelets were measured and their median values were statistically compared. Results: The majority of P. falciparum-infected patients (64.6%) showed a low-level parasitemia. The median values of Hb concentration, RBC count, mean corpuscular Hb and mean corpuscular Hb concentration were significantly lower in P. falciparum-infected patients, with anemia being significantly higher among infected patients than malaria-negative subjects (60.4% vs. 29.5%, respectively). The median total WBC count was non-significantly higher in P. falciparum-infected patients, with leucopenia being non-significantly different between both groups. The median platelet count was significantly lower in P. falciparum-infected patients, with thrombocytopenia being significantly higher among infected patients than malaria-negative subjects (72.4% vs. 5.0%, respectively).Conclusions: Most falciparum malaria infections among patients in Kosti city of the White Nile State – Sudan are of low-level parasitemia. Nevertheless, falciparum malaria is significantly associated with anemia and thrombocytopenia with lower median values of Hb, RBC count, MCH, MCHC and platelet count in P. falciparum-infected patients than malaria-negative subjects. In contrast, leucopenia is not useful to predict falciparum malaria. Further large-scale studies in community and healthcare settings and inclusion of patients with complicated or severe malaria and those with high parasite densities are recommended.

scholarly journals Peripheral Glycolysis in Neurodegenerative Diseases

2020 ◽  
Vol 21 (23) ◽  
pp. 8924 ◽  
Author(s):  
Simon M. Bell ◽  
Toby Burgess ◽  
James Lee ◽  
Daniel J. Blackburn ◽  
Scott P. Allen ◽  
...  
Keyword(s):  
Nervous System ◽  
Neurodegenerative Diseases ◽  
Neurodegenerative Disease ◽  
Blood Cells ◽  
Large Scale ◽  
White Blood Cells ◽  
Peripheral Cells ◽  
Multiple Patients ◽  
Brain And Spinal Cord ◽  
Lateral Sclerosis

Neurodegenerative diseases are a group of nervous system conditions characterised pathologically by the abnormal deposition of protein throughout the brain and spinal cord. One common pathophysiological change seen in all neurodegenerative disease is a change to the metabolic function of nervous system and peripheral cells. Glycolysis is the conversion of glucose to pyruvate or lactate which results in the generation of ATP and has been shown to be abnormal in peripheral cells in Alzheimer’s disease, Parkinson’s disease, and Amyotrophic Lateral Sclerosis. Changes to the glycolytic pathway are seen early in neurodegenerative disease and highlight how in multiple neurodegenerative conditions pathology is not always confined to the nervous system. In this paper, we review the abnormalities described in glycolysis in the three most common neurodegenerative diseases. We show that in all three diseases glycolytic changes are seen in fibroblasts, and red blood cells, and that liver, kidney, muscle and white blood cells have abnormal glycolysis in certain diseases. We highlight there is potential for peripheral glycolysis to be developed into multiple types of disease biomarker, but large-scale bio sampling and deciphering how glycolysis is inherently altered in neurodegenerative disease in multiple patients’ needs to be accomplished first to meet this aim.

scholarly journals Hematological Indices and Abnormalities Among Patients With Uncomplicated Falciparum Malaria in Kosti City of the White Nile State, Sudan: a Comparative Study

2021 ◽  
Author(s):  
Ahmed M. E. Elkhalifa ◽  
Rashad Abdul-Ghani ◽  
Abdelhakam G. Tamomh ◽  
Nur Eldin Eltaher ◽  
Nada Y. Ali ◽  
...  
Keyword(s):  
Platelet Count ◽  
Falciparum Malaria ◽  
Blood Cells ◽  
Large Scale ◽  
White Blood Cells ◽  
Hematological Indices ◽  
Low Level ◽  
White Nile ◽  
Hb Concentration ◽  
Rbc Count

Abstract Background: Hematological abnormalities are common features in falciparum malaria but vary among different populations across countries. Therefore, we compared hematological indices and abnormalities between Plasmodium falciparum-infected patients and malaria-negative subjects in Kosti city of the White Nile State, Sudan. Methods: A comparative, cross-sectional study was conducted at the Medical Technology Laboratory Unit of Kosti Teaching Hospital from June to December 2018. A total of 392 participants (192 P. falciparum-infected patients and 200 malaria-negative subjects) were recruited in the study. Hematological indices of hemoglobin (Hb), red blood cells (RBCs), white blood cells (WBCs) and platelets were measured and their median values were statistically compared.Results: The majority of P. falciparum-infected patients (64.6%) showed a low-level parasitemia. The median values of Hb concentration, RBC count, mean corpuscular Hb and mean corpuscular Hb concentration were significantly lower in P. falciparum-infected patients, with anemia being significantly higher among infected patients than malaria-negative subjects (60.4% vs. 29.5%, respectively). The median total WBC count was non-significantly higher in P. falciparum-infected patients, with leucopenia being non-significantly different between both groups. The median platelet count was significantly lower in P. falciparum-infected patients, with thrombocytopenia being significantly higher among infected patients than malaria-negative subjects (72.4% vs. 5.0%, respectively).Conclusions: Most falciparum malaria infections among patients in Kosti city of the White Nile State – Sudan are of low-level parasitemia. Nevertheless, falciparum malaria is significantly associated with anemia and thrombocytopenia with lower median values of Hb, RBC count, MCH, MCHC and platelet count in P. falciparum-infected patients than malaria-negative subjects. In contrast, leucopenia is not useful to predict falciparum malaria. Further large-scale studies in community and healthcare settings and inclusion of patients with complicated or severe malaria and those with high parasite densities are recommended.

Large Scale Graph Mining with MapReduce

2011 ◽  
pp. 299-314
Author(s):  
Charalampos E. Tsourakakis
Keyword(s):  
Survey Research ◽  
Present State ◽  
Real World ◽  
Graph Mining ◽  
Large Scale ◽  
State Of The Art ◽  
Research Work

In this Chapter, we present state of the art work on large scale graph mining using MapReduce. We survey research work on an important graph mining problem, counting the number of triangles in large-real world networks. We present the most important applications related to the count of triangles and two families of algorithms, a spectral and a combinatorial one, which solve the problem efficiently.

scholarly journals Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Khaled Almezhghwi ◽  
Sertan Serte
Keyword(s):  
Blood Cells ◽  
Data Augmentation ◽  
White Blood Cells ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Human Errors ◽  
Morphological Variations ◽  
Adversarial Network

White blood cells (leukocytes) are a very important component of the blood that forms the immune system, which is responsible for fighting foreign elements. The five types of white blood cells include neutrophils, eosinophils, lymphocytes, monocytes, and basophils, where each type constitutes a different proportion and performs specific functions. Being able to classify and, therefore, count these different constituents is critical for assessing the health of patients and infection risks. Generally, laboratory experiments are used for determining the type of a white blood cell. The staining process and manual evaluation of acquired images under the microscope are tedious and subject to human errors. Moreover, a major challenge is the unavailability of training data that cover the morphological variations of white blood cells so that trained classifiers can generalize well. As such, this paper investigates image transformation operations and generative adversarial networks (GAN) for data augmentation and state-of-the-art deep neural networks (i.e., VGG-16, ResNet, and DenseNet) for the classification of white blood cells into the five types. Furthermore, we explore initializing the DNNs’ weights randomly or using weights pretrained on the CIFAR-100 dataset. In contrast to other works that require advanced image preprocessing and manual feature extraction before classification, our method works directly with the acquired images. The results of extensive experiments show that the proposed method can successfully classify white blood cells. The best DNN model, DenseNet-169, yields a validation accuracy of 98.8%. Particularly, we find that the proposed approach outperforms other methods that rely on sophisticated image processing and manual feature engineering.

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