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    INTRAUTERINE GROWTH RESTRICTION (IUGR) RISK DECISION BASED ON SUPPORT VECTOR MACHINES
    (Mdpi, 2010) Zengin, Zeynep; Gurgen, Fikret; Varol, Fuesun
    This paper studies the risk of intrauterine growth restriction (IUGR) using support vector machines (SVM). A structured and globally optimized SVM system may be preferable procedure in the identification of IUGR fetus at risk. The IUGR risk is estimated in two stages: in the first stage, noninvasive Doppler pulsatility index (PI) and resistance index (RI) of umbilical artery (UA), middle cerebral artery (MCA) and ductus venosus (DV) and amniotic fluid index (AFI) are retrospectively analyzed and the Doppler indices are applied to the SVM system to make a diagnosis decision on the fetal wellbeing as reactive or nonreactive and/or acute fetal distress (AFD) on the nonstress test (NST) (training data). In the second stage (testing data), the decision is validated by the NST (target value). Experiments are performed on previously collected data. Fortyfour preterm with IUGR and without IUGR pregnancies before 34 weeks gestation are considered. The nonparametric Bayes-risk decision rule, k-nearest neighbor (k-NN), is used for comparison. It is observed that the SVM system is proven to be useful in predicting the expected risk in IUGR cases in this small population study. The PI and RI values of UA, MCA and DV are also effective in distinguishing IUGR at risk.
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    Intrauterine growth restriction (IUGR) risk decision based on support vector machines
    (Pergamon-Elsevier Science Ltd, 2012) Gurgen, Fikret; Zengin, Zeynep; Varol, Fusun
    This paper studies the risk of intrauterine compromise in the fetuses with intrauterine growth restriction (IUGR) using support vector machines (SVM). A structured and globally optimized SVM system may be preferable procedure in the identification of IUGR fetus at risk. The IUGR risk is estimated in two stages: In the first stage, noninvasive Doppler pulsatility index (PI) and resistance index (RI) of umbilical artery (UA), middle cerebral artery (MCA) and ductus venosus (DV), and amniotic fluid index (AFI) are retrospectively analyzed and the Doppler indices are applied to the SVM system to make a diagnosis decision on the fetal well being as reactive or nonreactive and/or fetal distress (FD) on the nonstress test (NST) (training data). In the second stage (testing data), the decision is validated by the NST (target value). Experiments are performed in retrospective clinical situation. Forty-four preterm with IUGR and without IUGR pregnancies before 34 weeks gestation are considered. Also, the nonparametric Bayes-risk decision rule, k-nearest neighbor (k-NN), is used for comparison. It is observed that the SVM system is proven to be useful in predicting the expected risk in IUGR cases in the small population study. Also, the PI and RI values of UA, MCA and DV are effective in distinguishing IUGR cases at risk. (C) 2011 Elsevier Ltd. All rights reserved.
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    Prenatal Risk Assessment of Trisomy 21 by Probabilistic Classifiers
    (IEEE, 2013) Uzun, Omer; Kaya, Heysem; Gurgen, Fikret; Varol, Fusun G.
    This study proposes a probabilistic approach to evaluate prenatal risk of Down syndrome. In this study, we address the decision-making problem in diagnosing Down syndrome from the machine learning perspective aiming to decrease invasive tests. We employ Naive Bayes and Bayesian Networks classification algorithms as probabilistic methods. This probabilistic classification approach is one of the leading work in medical domain. We use George Washington University dataset in our study. We also benchmark our probabilistic classifiers with widely used non-probabilistic classifiers in machine learning literature. Finally the results of the experiments show that probabilistic classifiers enable acceptable prediction of Trisomy 21 case and the classification performance can be improved by using the proposed techniques in this study.
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    A two-view ultrasound CAD system for spina bifida detection using Zernike features
    (Spie-Int Soc Optical Engineering, 2011) Konur, Umut; Gurgen, Fikret; Varol, Fusun
    In this work, we address a very specific CAD (Computer Aided Detection/Diagnosis) problem and try to detect one of the relatively common birth defects - spina bifida, in the prenatal period. To do this, fetal ultrasound images are used as the input imaging modality, which is the most convenient so far. Our approach is to decide using two particular types of views of the fetal neural tube. Transcerebellar head (i.e. brain) and transverse (axial) spine images are processed to extract features which are then used to classify healthy (normal), suspicious (probably defective) and non-decidable cases. Decisions raised by two independent classifiers may be individually treated, or if desired and data related to both modalities are available, those decisions can be combined to keep matters more secure. Even more security can be attained by using more than two modalities and base the final decision on all those potential classifiers. Our current system relies on feature extraction from images for cases (for particular patients). The first step is image preprocessing and segmentation to get rid of useless image pixels and represent the input in a more compact domain, which is hopefully more representative for good classification performance. Next, a particular type of feature extraction, which uses Zernike moments computed on either B/W or gray-scale image segments, is performed. The aim here is to obtain values for indicative markers that signal the presence of spina bifida. Markers differ depending on the image modality being used. Either shape or texture information captured by moments may propose useful features. Finally, SVM is used to train classifiers to be used as decision makers. Our experimental results show that a promising CAD system can be actualized for the specific purpose. On the other hand, the performance of such a system would highly depend on the qualities of image preprocessing, segmentation, feature extraction and comprehensiveness of image data.