Features Based IUGR Diagnosis Using Variational Level Set Method and Classification Using Artificial Neural Networks

Gadagkar, Akhilraj V.; Shreedhara, K. S.

doi:10.1109/icsip.2014.54

Cited by 12 publications

(12 citation statements)

References 1 publication

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“…Later, Magenes et al [62] proposed an SVM to detect IUGR using CTG data and showed good classification results in a cohort of 70 fetuses. In 2014, Gadagkar et al [63] developed an ANN system for the diagnosis of IUGR using only 2-D US morphometric measurements from almost 300 fetuses, and they had results similar to those obtained clinically in the same study population. Similarly, Rawat et al [64] implemented an ANN model using again 2-D US morphometric measurements from a total of 120 fetuses.…”

Section: For Fetal Diagnosismentioning

confidence: 82%

Machine Learning in Fetal Cardiology: What to Expect

et al. 2020

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In fetal cardiology, imaging (especially echocardiography) has demonstrated to help in the diagnosis and monitoring of fetuses with a compromised cardiovascular system potentially associated with several fetal conditions. Different ultrasound approaches are currently used to evaluate fetal cardiac structure and function, including conventional 2-D imaging and M-mode and tissue Doppler imaging among others. However, assessment of the fetal heart is still challenging mainly due to involuntary movements of the fetus, the small size of the heart, and the lack of expertise in fetal echocardiography of some sonographers. Therefore, the use of new technologies to improve the primary acquired images, to help extract measurements, or to aid in the diagnosis of cardiac abnormalities is of great importance for optimal assessment of the fetal heart. Machine leaning (ML) is a computer science discipline focused on teaching a computer to perform tasks with specific goals without explicitly programming the rules on how to perform this task. In this re-view we provide a brief overview on the potential of ML techniques to improve the evaluation of fetal cardiac function by optimizing image acquisition and quantification/segmentation, as well as aid in improving the prenatal diagnoses of fetal cardiac remodeling and abnormalities.

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Section: For Fetal Diagnosismentioning

confidence: 82%

Machine Learning in Fetal Cardiology: What to Expect

et al. 2020

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“…In 2014, the authors presented a new hybrid approach for detection of the IUGR fetus, using the variational level set method. Level set methods [ 82 ] are applied across fetus images for measuring the BPD and head region. The BPD and head circumference values are the test data for classification problem in the neural model.…”

Section: Future Trends Based On the Supervised Learning Methodsmentioning

confidence: 99%

Automated Techniques for the Interpretation of Fetal Abnormalities: A Review

Rawat¹,

Jain²,

Shrimali³

2018

Applied Bionics and Biomechanics

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Ultrasound (US) image segmentation methods, focusing on techniques developed for fetal biometric parameters and nuchal translucency, are briefly reviewed. Ultrasound medical images can easily identify the fetus using segmentation techniques and calculate fetal parameters. It can timely find the fetal abnormality so that necessary action can be taken by the pregnant woman. Firstly, a detailed literature has been offered on fetal biometric parameters and nuchal translucency to highlight the investigation approaches with a degree of validation in diverse clinical domains. Then, a categorization of the bibliographic assessment of recent research effort in the segmentation field of ultrasound 2D fetal images has been presented. The fetal images of high-risk pregnant women have been taken into the routine and continuous monitoring of fetal parameters. These parameters are used for detection of fetal weight, fetal growth, gestational age, and any possible abnormality detection.

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“…Foetuses of the first type tend to have a decrease in all dimensions of the body and internal organs, and usually face a higher risk of reduction in growth potential. The problems occur in the first or second trimester of pregnancy and are often encountered in foetuses with infection or genetic and anatomic defects [15]. The second type -asymmetrical -constitutes 75-80% of all cases born as IUGR.…”

Section: Introductionmentioning

confidence: 99%

“…IUGR may cause a significant risk factor for the development of many cardiovascular, metabolic, and pulmonologic diseases in adult life ( [8]- [10]). It is a challenging problem for obstetrician, neonatologists and pediatricians, as the diagnosis is based on non-consistent definitions (see [11] and [12]). It occurs in about 3-10% of live-born newborns, and the most serious problem of IUGR exists in developing countries where it concerns up to 20-30% of newborn infants [13].…”

Section: Introductionmentioning

confidence: 99%

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Supervised and Unsupervised Machine Learning for Improved Identification of Intrauterine Growth Restriction Types

Wosiak¹,

Zamecznik²,

Niewiadomska-Jarosik³

2016

Annals of Computer Science and Information Systems

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Abstract-This paper concerns automated identification of intrauterine growth restriction (IUGR) types by use of machine learning methods. The research presents a comparison of supervised and unsupervised learning covering single and hybrid classification, as well as clustering. Supervised learning techniques included bagging with Naïve Bayes, k-nearest neighbours (kNN), C4.5 and SMO as base classifiers, random forest as a variant of bagging with a decision tree as a base classifier, boosting with Naïve Bayes, SMO, kNN and C4.5 as base classifiers, and voting by all single classifiers using majority as a combination rule, as well as five single classification strategies: kNN, C4.5, Naïve Bayes, random tree and sequential minimal optimization algorithm for training support vector machines. Unsupervised learning encompassed k-means and expectation-maximization algorithms. The major conclusion drawn from the study was that hybrid classifiers have demonstrated their potential ability to identify more accurately symmetrical and asymmetrical types of IUGR, whereas the unsupervised learning techniques produced the worst results.

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Features Based IUGR Diagnosis Using Variational Level Set Method and Classification Using Artificial Neural Networks

Cited by 12 publications

References 1 publication

Machine Learning in Fetal Cardiology: What to Expect

Machine Learning in Fetal Cardiology: What to Expect

Automated Techniques for the Interpretation of Fetal Abnormalities: A Review

Supervised and Unsupervised Machine Learning for Improved Identification of Intrauterine Growth Restriction Types

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