Identification of Sickle Cell Anemia Using Deep Neural Networks

Yeruva, Sagar; Varalakshmi, M. Sharada; Gowtham, B. Pavan; Chandana, Yendluri Hari; Prasad, Pesn. Krishna

doi:10.28991/esj-2021-01270

Cited by 32 publications

(23 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar work has been done by Yeruva, S., Varalakshmi, M.S., Gowtham, B.P., Chandana, Y.H., and Prasad, P.K. for classifying Cell Anemia using a Deep neural network [23].…”

Section: -Literature Reviewmentioning

confidence: 61%

Support Directional Shifting Vector: A Direction Based Machine Learning Classifier

et al. 2021

View full text Add to dashboard Cite

Machine learning models have been very popular nowadays for providing rigorous solutions to complicated real-life problems. There are three main domains named supervised, unsupervised, and reinforcement. Supervised learning mainly deals with regression and classification. There exist several types of classification algorithms, and these are based on various bases. The classification performance varies based on the dataset velocity and the algorithm selection. In this article, we have focused on developing a model of angular nature that performs supervised classification. Here, we have used two shifting vectors named Support Direction Vector (SDV) and Support Origin Vector (SOV) to form a linear function. These vectors form a linear function to measure cosine-angle with both the target class data and the non-target class data. Considering target data points, the linear function takes such a position that minimizes its angle with target class data and maximizes its angle with non-target class data. The positional error of the linear function has been modelled as a loss function which is iteratively optimized using the gradient descent algorithm. In order to justify the acceptability of this method, we have implemented this model on three different standard datasets. The model showed comparable accuracy with the existing standard supervised classification algorithm. Doi: 10.28991/esj-2021-01306 Full Text: PDF

show abstract

“…Similar work has been done by Yeruva, S., Varalakshmi, M.S., Gowtham, B.P., Chandana, Y.H., and Prasad, P.K. for classifying Cell Anemia using a Deep neural network [23].…”

Section: -Literature Reviewmentioning

confidence: 61%

Support Directional Shifting Vector: A Direction Based Machine Learning Classifier

et al. 2021

View full text Add to dashboard Cite

show abstract

“…These abnormally shaped RBCs can stick together, increasing viscosity, impeding blood flow, and causing microvascular end-organ damage and vaso-occlusive pain 19-22. The sickle-shaped cells also can adhere to vascular walls, increasing the risk for vaso-occlusive events 1,19,20,22…”

Section: Pathophysiologymentioning

confidence: 99%

“…[19][20][21][22] These abnormally shaped RBCs can stick together, increasing viscosity, impeding blood fl ow, and causing microvascular end-organ damage and vaso-occlusive pain. [19][20][21][22] The sickle-shaped cells also can adhere to vascular walls, increasing the risk for vasoocclusive events. 1,19,20,22 In a person with one of the life-altering SCD genotypes (HbSS, HbSC, and HbS beta-thalassemia), numerous triggers can cause RBCs to sickle, resulting in progressive vaso-occlusion, pain, and complications.…”

mentioning

confidence: 99%

An outpatient pain plan and ED pain pathway for adults with sickle cell disease

Mercado¹

2023

Jaapa

View full text Add to dashboard Cite

morbidity and early mortality with a reduced life expectancy of about 54 years. 1-3 SCD affects primarily Black patients and, to a lesser degree, Hispanic patients, affecting more than 100,000 patients in the United States. 2,4,5 Many of these patients are from lower socioeconomic backgrounds, have limited access to care, and are exposed to racial bias. [5][6][7][8][9][10] Because of their recurrent pain crises, patients with SCD may be labeled as drug-seeking and may not receive necessary care when presenting to the ED. 11,12 In addition, patients with SCD have less access to comprehensive team-based healthcare than patients with other genetic disorders such as hemophilia or cystic fi brosis. 10 To highlight SCD and improve the lives of patients with chronic and acute pain, in 2014, the National Heart, Lung, and Blood Institute (NHLBI) developed the seminal guidelines for managing chronic and acute SCD pain. 13 In 2020, the American Society of Hematology (ASH) updated the SCD pain management guidelines in an increased effort to enhance clinician education. 14 Additionally, in 2021, the American College of Emergency Physicians (ACEP) created an effective pain management plan to provide ED clinicians with evidence-based acute pain management guidelines. 11,14,15 Despite these practical pathways for managing vaso-occlusive pain in SCD, studies show that healthcare disparities continue. 3,5,8,11,[15][16][17] The disparities may be attributed to a lack of clinician confi dence, knowledge, or experience with acute pain crises or the unique clinical needs of patients with SCD. 3,9,11,12,18 Although major healthcare organizations have created © VICTOR JOSAN/SHUTTERSTOCK.COM S ickle cell disease (SCD) is a common inherited monogenic disease with many genotypes. The lifealtering genotypes causing the most complications include hemoglobin (Hb) SS, HbSC, and HbS betathalassemia. [1][2][3] These genotypes lead to signifi cant lifetime ABSTRACT Sickle cell disease (SCD), one of the most common inherited diseases, is associated with lifetime morbidity and reduced life expectancy. In the United States, SCD primarily affects Black patients and, to a lesser degree, those of Hispanic descent. These populations are known to have healthcare disparities related to lower socioeconomic status, limited access to healthcare, and racial bias. The quality-adjusted life expectancy of patients with SCD is less than 35 years, because of progressive complications of the disease. The most common complication is severe episodic pain related to vaso-occlusive ischemic events. Despite guidelines, pain management often is delayed as patients struggle with resistance from clinicians based on concerns over opioid use or abuse, overdose, or drug-seeking behavior. Effective pain management can be accomplished with collaboration between clinicians and patients, a documented outpatient pain management plan, and when necessary, an ED clinical pain pathway for acute SCD pain management.

show abstract

“…They classified it according to the shape of the cells as sickle cell, thalassemia, and normal. As a method, they compared the multilayer perceptron method with other machine learning methods and stated that they were successful [42]. Gupta et al conducted a study that detected the presence of covid 19 pneumonia, a new disease that affects millions of people around the world.…”

Section: Related Workmentioning

confidence: 99%

Diagnosing Hematological Disorders Using Deep Learning Method

Karagül

Yurtay

Öneç

2021

Sakarya University Journal of Computer and Information Sciences

View full text Add to dashboard Cite

Deciding on the diagnosis of the disease is an important step for treating the patients. Also, the numerical value of blood tests, the personal information of patients, and most importantly, an expert opinion is necessary to diagnose a disease. With the development of technology, patient-related data are obtained both rapidly and in large sizes. Deep learning methods, which can produce meaningful results by processing the data in raw form, are beginning to give results that are close to human opinion nowadays. The present work is aimed to develop a system that will enable the diagnosis of anemia in general practice conditions due to the increasing number of patients and the intention of the hospitals, as well as the difficulties in reaching the expert medical consultant. The main contribution of this work is to make a diagnosis like a doctor with the data as the way the doctor uses it. The data set was obtained from the actual hospital environment and no intervention, such as increasing or decreasing the number of data, increasing or decreasing the number of attributes, reduction, integration, imputation, transformation, or discretization, has been made on the incoming patient data. The original hospital data are classified for the diagnosis of anemia types and the accuracy of 84,97% achieved by using a deep learning algorithm.

show abstract

Identification of Sickle Cell Anemia Using Deep Neural Networks

Cited by 32 publications

References 22 publications

Support Directional Shifting Vector: A Direction Based Machine Learning Classifier

Support Directional Shifting Vector: A Direction Based Machine Learning Classifier

An outpatient pain plan and ED pain pathway for adults with sickle cell disease

Diagnosing Hematological Disorders Using Deep Learning Method

Contact Info

Product

Resources

About