Malaria Parasitaemia Estimation � A new perspective (Preprint)

2018 ◽  
Author(s):  
Samir Bandyopadhyay ◽  
Sanjay Nag ◽  
Nabanita Basu
Keyword(s):  
Blood Cell ◽  
Sample Size ◽  
Blood Cells ◽  
Malaria Parasite ◽  
Blood Smear ◽  
White Blood Cells ◽  
Particle Identification ◽  
Colour Space ◽  
The Third ◽  
Parasite Detection

BACKGROUND Malaria has plagued tropical, developing countries over the last two centuries. Light Microscopy is the gold standard for malaria parasite detection. This research work is aimed to harness the potential of virtual microscopy and computer aided diagnosis systems to minimize human error and labour towards malaria parasite detection. OBJECTIVE The proposed method is tested on differently stained blood smear images for malaria parasite detection. METHODS Digitized thin blood smears have been used to predict the presence of malaria parasite using unsupervised and rule based methods. A dataset consisting of 1410 images (667 infected, 743 normal) was developed from the MaMic database. Cochrane’s sample size estimation was used to decide sample size. To widen the applicability of the algorithm beyond the dataset under consideration, illumination correction, database specific artefact removal was performed. Thereafter unsupervised k-means (k=3) clustering was performed to segregate the foreground components, the erythrocyte, malaria infection and white blood cells from the background. Clumps are identified based on the third quartile bound of the area distribution of the foreground components. The clumps consist of both, red blood cell clumps and mixed clumps consisting of both red and white blood cells. Clumps marked out were de-clumped automatically using modified watershed algorithm. The binary de-clumped mask was used to retrieve pixel colour information from the original image. The image colour in RGB colour space was down sampled by representing the same in YCbCr colour space. Based on the values in YCbCr colour space, the image was recoloured and pixel position matching was performed to detect malaria parasite. RESULTS As compared to Zack’s thresholding (63.75%), 3-means clustering (98.96%) had a higher accuracy at foreground particle identification. The third quartile mark was selected for clump/s identification while Tukey’s upper hinge showed higher strength towards white blood cell particle identification. The accuracy for malaria parasite detection by the proposed system was recorded as 98.11% (Sensitivity-0.9645, Specificity-1, AUC-0.9583) CONCLUSIONS The proposed work is particularly innovative as it uses two basic features, colour and area, to identify malaria parasite in thin blood smear image. The paper documents an automated robust algorithm to assist pathologists at Parasitaemia estimation as per World Health Organization standard.

scholarly journals Blood Cell Count Using Deep Learning Semantic Segmentation

2019 ◽  
Author(s):  
Thanh Tran ◽  
Lam Binh Minh ◽  
Suk-Hwan Lee ◽  
Ki-Ryong Kwon
Keyword(s):  
Deep Learning ◽  
Blood Cell ◽  
Cell Count ◽  
Blood Cells ◽  
Blood Smear ◽  
White Blood Cells ◽  
Semantic Segmentation ◽  
Peripheral Blood Smear ◽  
Cell Counting ◽  
Blood Cell Count

Clinically, knowing the number of red blood cells (RBCs) and white blood cells (WBCs) helps doctors to make the better decision on accurate diagnosis of numerous diseases. The manual cell counting is a very time-consuming and expensive process, and it depends on the experience of specialists. Therefore, a completely automatic method supporting cell counting is a viable solution for clinical laboratories. This paper proposes a novel blood cell counting procedure to address this challenge. The proposed method adopts SegNet - a deep learning semantic segmentation to simultaneously segment RBCs and WBCs. The global accuracy of the segmentation of WBCs, RBCs, and the background of peripheral blood smear images obtains 89% when segment WBCs and RBCs from the background of blood smear images. Moreover, an effective solution to separate grouped or overlapping cells and cell count is presented using Euclidean distance transform, local maxima, and connected component labeling. The counting result of the proposed procedure achieves an accuracy of 93.3% for red blood cell count using dataset 1 and 97.38% for white blood cell count using dataset 2.

scholarly journals Classifying White Blood Cells in Blood Smear Images using a Convolutional Neural Network

2019 ◽  
Vol 8 (9S) ◽  
pp. 825-828
Keyword(s):  
Neural Network ◽  
Blood Cell ◽  
Convolutional Neural Network ◽  
White Blood Cell ◽  
Blood Cells ◽  
Blood Smear ◽  
Binary Classification ◽  
White Blood Cells ◽  
Classification Problem ◽  
Classification Task

We have tried to automate the classification task of white blood cells by using a Convolutional Neural Network. We have divided white blood cell classification in two types of problems, a binary class problem and a 4-classification problem. In binary class problem we classify white blood cell as either mononuclear or Grenrecules. In 4-classification problem where cells are classified into their subtypes (monocytes, lymphocytes, neutrophils, basophils and eosinophils). In our experiment we were able to achieve validation accuracy of 100% in binary classification and 98.40 in multiple classifications.

scholarly journals Lightweight Deep Learning for Malaria Parasite Detection Using Cell-Image of Blood Smear Images

2020 ◽  
Vol 34 (5) ◽  
pp. 571-576
Author(s):  
Amin Alqudah ◽  
Ali Mohammad Alqudah ◽  
Shoroq Qazan
Keyword(s):  
Blood Cells ◽  
Malaria Parasite ◽  
Blood Smear ◽  
Machine Learning Algorithms ◽  
Infected Female ◽  
Plasmodium Parasite ◽  
Cell Image ◽  
Input Size ◽  
Parasite Detection ◽  
Global Population

Malaria is an infectious disease that is caused by the plasmodium parasite which is a single-celled group. This disease is usually spread employing an infected female anopheles mosquito. Recent statistics show that in 2017 there were only around 219 million recorded cases and about 435,000 deaths were reported due to this disease and more than 40% of the global population is at risk. Despite this, many image processing fused with machine learning algorithms were developed by researchers for the early detection of malaria using blood smear images. This research used a new CNN model using transfer learning for classifying segmented infected and Uninfected red blood cells. The experimental results show that the proposed architecture success to detect malaria with an accuracy of 98.85%, sensitivity of 98.79%, and a specificity of 98.90% with the highest speed and smallest input size among all previously used CNN models.

scholarly journals A case of acute leukaemia in a 38-years old female

2012 ◽  
Vol 18 (2) ◽  
pp. 83-85 ◽  
Author(s):  
Barbu Adina
Keyword(s):  
Stem Cells ◽  
Bone Marrow ◽  
Blood Cell ◽  
Microscopic Examination ◽  
Blood Cells ◽  
Acute Leukaemia ◽  
Blood Smear ◽  
White Blood Cells ◽  
Peripheral Blood Smear ◽  
Large Numbers

Abstract Leukaemia is cancer that starts in blood-forming tissues, such as bone marrow, and causes large numbers of abnormal blood cells to be produced and enter the bloodstream. The stem cells usually develop into a type of white blood cell called myeloblasts which do not mature into healthy white blood cells. The leukaemia cells are unable to do their usual work and can build up in the blood and bone marrow so there is less space for healthy white blood cells, red blood cells and platelets. Anemia is a major sign but diagnosis is provided only microscopic examination of peripheral blood smear.

DETERMINATION OF WHITE BLOOD CELLS USING FOLDSCOPE WITH SMARTPHONE

2019 ◽  
pp. 10-13
Author(s):  
Ranu Kumar ◽  
Prasad Kapildeo
Keyword(s):  
Human Blood ◽  
Blood Cells ◽  
Blood Smear ◽  
Clinical Laboratory ◽  
White Blood Cells ◽  
Healthcare Services ◽  
Field Level ◽  
Morphology Analysis

We are traditionally used Microscope in clinical laboratory for determination of white blood cells of human blood smear. Now, in this study we were used Foldscope with Smartphone in the place of Microscope and examine many samples of human blood smear which was collected from local diagnostic centers. We were very easily quantity & morphology analysis of all types of WBC cells such as Neutrophils, Lymphocytes, Monocytes, Eosionophils, Basophils in blood smear with the help of Foldscope & image taken by Smartphone. The main objective of this study is to use Foldscope for quantity & morphology analysis of human WBCs at field level especially poor resource area where healthcare services or centers is not available & where carry of microscope is not possible.

PENENTUAN KRITERIA PENILAIAN KESAN JUMLAH LEUKOSIT PADA PEMERIKSAAN APUSAN DARAH TEPI

2018 ◽  
Vol 3 (2) ◽  
pp. 52-61
Author(s):  
Dzikra Arwie ◽  
Islawati
Keyword(s):  
Data Analysis ◽  
Statistical Analysis ◽  
Blood Cells ◽  
Blood Smear ◽  
White Blood Cells ◽  
Peripheral Blood Smear ◽  
Field Of View ◽  
Laboratory Observation ◽  
Number Of Cells

Leukocytes or white blood cells have a characteristic characteristic of different cells. Determination of the impression of the number of leukocytes is determined in the number of cells in the field of view. While the number of viewable field cells expressed is still quite varied. The purpose of this study was to determine the number of leukocytes in the field of view and expressed the impression of a sufficient amount. This research was conducted at the Laboratory of Health Analyst Department Panrita Husada Bulukumba on 9 April 2017 to 14 July 2017. This type of research is a laboratory observation that aims to determine the criteria for assessing the impression of the number of leukocytes on a peripheral blood smear. Data analysis using statistical analysis is the average and standard deviations to determine the impression of the number of leukocytes and use 3 inspection zones. The results of this study obtained results in zone IV the number of leukocyte impressions said to be sufficient was 7-10, in zone V the number of leukocyte impressions said to be sufficient was 4-9, and in zone VI the number of leukocyte impressions said to be sufficient was 3-8.  

scholarly journals White Blood Cells and Severe COVID-19: A Mendelian Randomization Study

2021 ◽  
Vol 11 (3) ◽  
pp. 195
Author(s):  
Yitang Sun ◽  
Jingqi Zhou ◽  
Kaixiong Ye
Keyword(s):  
Blood Cell ◽  
Cell Count ◽  
White Blood Cell Count ◽  
White Blood Cell ◽  
Blood Cells ◽  
Mendelian Randomization ◽  
Association Studies ◽  
White Blood Cells ◽  
Genome Wide Association Studies ◽  
Blood Cell Count

Increasing evidence shows that white blood cells are associated with the risk of coronavirus disease 2019 (COVID-19), but the direction and causality of this association are not clear. To evaluate the causal associations between various white blood cell traits and the COVID-19 susceptibility and severity, we conducted two-sample bidirectional Mendelian Randomization (MR) analyses with summary statistics from the largest and most recent genome-wide association studies. Our MR results indicated causal protective effects of higher basophil count, basophil percentage of white blood cells, and myeloid white blood cell count on severe COVID-19, with odds ratios (OR) per standard deviation increment of 0.75 (95% CI: 0.60–0.95), 0.70 (95% CI: 0.54–0.92), and 0.85 (95% CI: 0.73–0.98), respectively. Neither COVID-19 severity nor susceptibility was associated with white blood cell traits in our reverse MR results. Genetically predicted high basophil count, basophil percentage of white blood cells, and myeloid white blood cell count are associated with a lower risk of developing severe COVID-19. Individuals with a lower genetic capacity for basophils are likely at risk, while enhancing the production of basophils may be an effective therapeutic strategy.

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