scholarly journals Deep Learning Models for Classification of Red Blood Cells in Microscopy Images to Aid in Sickle Cell Anemia Diagnosis

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 427 ◽  
Author(s):  
Laith Alzubaidi ◽  
Mohammed A. Fadhel ◽  
Omran Al-Shamma ◽  
Jinglan Zhang ◽  
Ye Duan
Keyword(s):  
Deep Learning ◽  
Red Blood Cells ◽  
Transfer Learning ◽  
Sickle Cell ◽  
Sickle Cell Anemia ◽  
Blood Cells ◽  
Learning Models ◽  
Sickle Cells ◽  
Microscopy Images

Sickle cell anemia, which is also called sickle cell disease (SCD), is a hematological disorder that causes occlusion in blood vessels, leading to hurtful episodes and even death. The key function of red blood cells (erythrocytes) is to supply all the parts of the human body with oxygen. Red blood cells (RBCs) form a crescent or sickle shape when sickle cell anemia affects them. This abnormal shape makes it difficult for sickle cells to move through the bloodstream, hence decreasing the oxygen flow. The precise classification of RBCs is the first step toward accurate diagnosis, which aids in evaluating the danger level of sickle cell anemia. The manual classification methods of erythrocytes require immense time, and it is possible that errors may be made throughout the classification stage. Traditional computer-aided techniques, which have been employed for erythrocyte classification, are based on handcrafted features techniques, and their performance relies on the selected features. They also are very sensitive to different sizes, colors, and complex shapes. However, microscopy images of erythrocytes are very complex in shape with different sizes. To this end, this research proposes lightweight deep learning models that classify the erythrocytes into three classes: circular (normal), elongated (sickle cells), and other blood content. These models are different in the number of layers and learnable filters. The available datasets of red blood cells with sickle cell disease are very small for training deep learning models. Therefore, addressing the lack of training data is the main aim of this paper. To tackle this issue and optimize the performance, the transfer learning technique is utilized. Transfer learning does not significantly affect performance on medical image tasks when the source domain is completely different from the target domain. In some cases, it can degrade the performance. Hence, we have applied the same domain transfer learning, unlike other methods that used the ImageNet dataset for transfer learning. To minimize the overfitting effect, we have utilized several data augmentation techniques. Our model obtained state-of-the-art performance and outperformed the latest methods by achieving an accuracy of 99.54% with our model and 99.98% with our model plus a multiclass SVM classifier on the erythrocytesIDB dataset and 98.87% on the collected dataset.

Short survival of phosphatidylserine-exposing red blood cells in murine sickle cell anemia

Blood ◽  
2001 ◽  
Vol 98 (5) ◽  
pp. 1577-1584 ◽  
Author(s):  
Kitty de Jong ◽  
Renee K. Emerson ◽  
James Butler ◽  
Jacob Bastacky ◽  
Narla Mohandas ◽  
...  
Keyword(s):  
Red Blood Cells ◽  
Sickle Cell ◽  
Sickle Cell Anemia ◽  
Blood Cells ◽  
Cell Model ◽  
Red Cell ◽  
Sickle Cells ◽  
Red Cell Survival ◽  
Transgenic Murine Models

Several transgenic murine models for sickle cell anemia have been developed that closely reproduce the biochemical and physiological disorders in the human disease. A comprehensive characterization is described of hematologic parameters of mature red blood cells, reticulocytes, and red cell precursors in the bone marrow and spleen of a murine sickle cell model in which erythroid cells expressed exclusively human α, γ, and βS globin. Red cell survival was dramatically decreased in these anemic animals, partially compensated by considerable enhancement in erythropoietic activity. As in humans, these murine sickle cells contain a subpopulation of phosphatidylserine-exposing cells that may play a role in their premature removal. Continuous in vivo generation of this phosphatidylserine-exposing subset may have a significant impact on the pathophysiology of sickle cell disease.

scholarly journals Adhesion of sickle red blood cells and damage to interleukin-1 beta stimulated endothelial cells under flow in vitro

Blood ◽  
1996 ◽  
Vol 87 (11) ◽  
pp. 4845-4852 ◽  
Author(s):  
M Natarajan ◽  
MM Udden ◽  
LV McIntire
Keyword(s):  
Endothelial Cells ◽  
Endothelial Cell ◽  
Red Blood Cells ◽  
Sickle Cell ◽  
Sickle Cell Anemia ◽  
Blood Cells ◽  
Prolonged Exposure ◽  
Sickle Cells ◽  
Sickle Erythrocytes ◽  
Sickle Red Blood Cells

Two factors that are hypothesized to contribute to vasoocclusive crises in sickle cell anemia are increased sickle red blood cell-endothelial cell interactions and damage to endothelium. Despite considerable study, the mechanisms by which erythrocyte-endothelial interactions occur and the role of endothelial damage have not yet been fully elucidated. In this report, we demonstrate that adhesion and damage may be related in a model of vasoocclusion in sickle cell anemia. Phase contrast microscopy coupled to digital image processing was used to determine the adhesion of sickle red blood cells to 1-, 4-, and 24-hour interleukin-I beta (IL-1 beta) stimulated endothelial calls in a parallel plate flow chamber. Morphological alterations to activated endothelial cells after the perfusion of sickle erythrocytes were also identified. Pretreatment of monolayers with 50 pg/mL of IL-1 beta for 1, 4, and 24 hours caused approximately 16-fold increases in adhesion of sickle cells to activated endothelium at all time points. Results with an Arginine-glycine aspartic acid (RGD) peptide and monoclonal antibodies indicated a role for three different endothelial cell receptors: alpha v beta 3 after 1 hour of IL-1 beta stimulation; E- selectin after 4 hours of IL-1 beta stimulation; and vascular cell adhesion molecule-1 after prolonged exposure to cytokines. Perfusion of sickle, but not normal, erythrocytes resulted in alteration of endothelial morphology. Approximately 6% to 8% damage was observed on 4- and 24-hour IL-1 beta stimulated endothelial cells after the perfusion of sickle cells. Damage to 24-hour activated endothelial cells showed a positive correlation (r = .899) with the number of adherent sickle erythrocytes.

scholarly journals A deep convolutional neural network for classification of red blood cells in sickle cell anemia

2017 ◽  
Vol 13 (10) ◽  
pp. e1005746 ◽  
Author(s):  
Mengjia Xu ◽  
Dimitrios P. Papageorgiou ◽  
Sabia Z. Abidi ◽  
Ming Dao ◽  
Hong Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Red Blood Cells ◽  
Sickle Cell ◽  
Sickle Cell Anemia ◽  
Blood Cells ◽  
Deep Convolutional Neural Network

scholarly journals A deep learning AlexNet model for classification of red blood cells in sickle cell anemia

2020 ◽  
Vol 9 (2) ◽  
pp. 221
Author(s):  
Hajara Aliyu Abdulkarim ◽  
Mohd Azhar Abdul Razak ◽  
Rubita Sudirman ◽  
Norhafizah Ramli
Keyword(s):  
Deep Learning ◽  
Red Blood Cells ◽  
Sickle Cell ◽  
Sickle Cell Anemia ◽  
Blood Cells ◽  
Region Of Interest ◽  
Blood Film ◽  
Two Phase ◽  
Cell Shapes ◽  
Sensitivity Specificity

Sickle cell anemia (SCA) is a serious hematological disorder, where affected patients are frequently hospitalized throughout a lifetime and even can cause death. The manual method of detecting and classifying abnormal cells of SCA patient blood film through a microscope is time-consuming, tedious, prone to error, and require a trained hematologist. The affected patient has many cell shapes that show important biomechanical characteristics. Hence, having an effective way of classifying the abnormalities present in the SCA disease will give a better insight into managing the concerned patient's life. This work proposed algorithm in two-phase firstly, automation of red blood cells (RBCs) extraction to identify the RBC region of interest (ROI) from the patient’s blood smear image. Secondly, deep learning AlexNet model is employed to classify and predict the abnormalities presence in SCA patients. The study was performed with (over 9,000 single RBC images) taken from 130 SCA patient each class having 750 cells. To develop a shape factor quantification and general multiscale shape analysis. We reveal that the proposed framework can classify 15 types of RBC shapes including normal in an automated manner with a deep AlexNet transfer learning model. The cell's name classification prediction accuracy, sensitivity, specificity, and precision of 95.92%, 77%, 98.82%, and 90% were achieved, respectively.

scholarly journals Identification of Sickle Cell Anemia Using Deep Neural Networks

2021 ◽  
Vol 5 (2) ◽  
pp. 200-210
Author(s):  
Sagar Yeruva ◽  
M. Sharada Varalakshmi ◽  
B. Pavan Gowtham ◽  
Y. Hari Chandana ◽  
PESN. Krishna Prasad
Keyword(s):  
Red Blood Cells ◽  
Sickle Cell ◽  
Sickle Cell Anemia ◽  
Blood Cells ◽  
Human Life ◽  
Organ Damage ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Healthy Human ◽  
Sickle Cells

A molecule called hemoglobin is found in red blood cells that holds oxygen all over the body. Hemoglobin is elastic, round, and stable in a healthy human. This makes it possible to float across red blood cells. But the composition of hemoglobin is unhealthy if you have sickle cell disease. It refers to compact and bent red blood cells. The odd cells obstruct the flow of blood. It is dangerous and can result in severe discomfort, organ damage, heart strokes, and other symptoms. The human life expectancy can be shortened as well. The early identification of sickle calls will help people recognize signs that can assist antibiotics, supplements, blood transfusion, pain-relieving medications, and treatments etc. The manual assessment, diagnosis, and cell count are time consuming process and may result in misclassification and count since millions of red blood cells are in one spell. When utilizing data mining techniques such as the multilayer perceptron classifier algorithm, sickle cells can be effectively detected with high precision in the human body. The proposed approach tackles the limitations of manual research by implementing a powerful and efficient MLP (Multi-Layer Perceptron) classification algorithm that distinguishes Sickle Cell Anemia (SCA) into three classes: Normal (N), Sickle Cells(S) and Thalassemia (T) in red blood cells. This paper also presents the precision degree of the MLP classifier algorithm with other popular mining and machine learning algorithms on the dataset obtained from the Thalassemia and Sickle Cell Society (TSCS) located in Rajendra Nagar, Hyderabad, Telangana, India. Doi: 10.28991/esj-2021-01270 Full Text: PDF

scholarly journals Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images

2021 ◽  
Vol 11 (5) ◽  
pp. 2284
Author(s):  
Asma Maqsood ◽  
Muhammad Shahid Farid ◽  
Muhammad Hassan Khan ◽  
Marcin Grzegorzek
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Red Blood Cells ◽  
Convolutional Neural Networks ◽  
Blood Cells ◽  
Superior Performance ◽  
Learning Models ◽  
Infected Female ◽  
The World ◽  
Augmentation Techniques

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 vastly scalable for image classification tasks that extract features through hidden layers of the model without any handcrafting. The detection of malaria-infected red blood cells from segmented microscopic blood images using convolutional neural networks can assist in quick diagnosis, and this will be useful for regions with fewer healthcare experts. The contributions of this paper are two-fold. First, we evaluate the performance of different existing deep learning models for efficient malaria detection. Second, we propose a customized CNN model that outperforms all observed deep learning models. It exploits the bilateral filtering and image augmentation techniques for highlighting features of red blood cells before training the model. Due to image augmentation techniques, the customized CNN model is generalized and avoids over-fitting. All experimental evaluations are performed on the benchmark NIH Malaria Dataset, and the results reveal that the proposed algorithm is 96.82% accurate in detecting malaria from the microscopic blood smears.

scholarly journals Elastic property of sickle cell anemia and sickle cell trait red blood cells

2021 ◽  
Vol 26 (09) ◽  
Author(s):  
Endris Muhammed ◽  
James Cooper ◽  
Daniel Devito ◽  
Robert Mushi ◽  
Maria del Pilar Aguinaga ◽  
...  
Keyword(s):  
Elastic Property ◽  
Red Blood Cells ◽  
Sickle Cell ◽  
Sickle Cell Anemia ◽  
Blood Cells ◽  
Sickle Cell Trait

HENNA (Lawsonia inermis) A MEDICINAL HERB EFFECTIVE IN SICKLE CELL DISEASE

2021 ◽  
pp. 01-04
Author(s):  
KRISHNA KUMAR ◽  
Nitish Kumar ◽  
Amresh gupta ◽  
Arpita singh ◽  
Pandey Swarnima ◽  
...  
Keyword(s):  
Sickle Cell Disease ◽  
Red Blood Cells ◽  
Sickle Cell ◽  
Blood Cells ◽  
Oxygen Affinity ◽  
Common Disease ◽  
Cell Disease ◽  
Lawsonia Inermis ◽  
Sodium Bisulphite ◽  
Sickle Cells

Sickle cell anemia is a common disease in Oman country. In this disease, sickle-shaped cells are formed. These cells interrupt blood vessels and cause a reduction in oxygen transportation. It was founded that henna (Lawsonia inermis) can prohibit the formation of sickle cells. The Lawsone (2-Hydroxy-1,4-Naphthoquinone) is the constituents of henna which is responsible for the anti-sickling activity, by increasing the oxygen affinity of red blood cells. Hena has the anti-sickling activity which is proved by incubating aqueous and methanolic henna extracts with sickle cell disease patient's whole blood. Then for reduction to oxygen tension 2%, sodium bisulphite was added. Therefore, the percentage of sickled cells to normal red blood cells was observed at 30 minutes intervals. Henna proved a delay in the sickling process in 84% of the tested samples. Both extracts(aqueous and methanolic henna) can delay sickling for about an hour.

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