Supervised classification approach of biometric measures for automatic fetal defect screening in head ultrasound images

Sahli, Hanene; Mouelhi, Aymen; Slama, Amine Ben; Sayadi, Mounir; Rachdi, R.

doi:10.1080/03091902.2019.1653389

Cited by 15 publications

(14 citation statements)

References 15 publications

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“…Traditional Image Processing methods (Chakkarwar, Joshi & Revankar, 2010;Wee et al, 2010;Rawat et al, 2013;Ibrahim et al,2017;Lu et al, 2005;Satwika et al, 2013;Foi et al, 2014;Sahili et al, 2019;Rasheed et al, 2021;Thomas, Peters & Jeanty, 1991;Mukherjee et al, 2010;Wang, 2014;Methews & Deepa, 2014;Khan et al, 2015;Yu, Wang & Chen, 2015;Amoah, Anto & Crimi, 2015;Ravishankar, Prabhu & Vaidya, 2016;Hermawati et al, 2019;Yu et al, 2008;Wang et al, 2014;Jang et al, 2018;Kim et al, 2018;Nirmala & Palanisami, 2010;Lakshmi et al, 2008;Karl, Kagan & Chaoui, 2012;Sonia & Shanti, 2015;Deng et al, 2012;Anzalone et al, 2013;Sonia & Shanti, 2016;Nie et al, 2017) Supervised Learning methods (Khazendar et al, 2014;Ibrahim et al, 2017;Lu et al, 2005;Sahli et al, 2019;Carneiro et al, 2008;Anjit & Rishidas, 2011;Rafeek & Gunasundari, 2013;Deng et al, 2012;Park et al, 2013;Sciortino, Tegolo & Valenti, 2017) Unsupervised Learning methods…”

Section: Classification Of Existing Studies According To the Techniqu...mentioning

confidence: 99%

Computer-Aided Monitoring of Fetus Health From Ultrasound Images: A Review

Ataş¹,

Yılmaz²

2022

acin

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Computer aided diagnostic methods have been helping medical experts for monitoring fetus health for many years. The use of new methods has made a positive effect in monitoring the health of fetus as well as the diagnosis of anomalies. This study first introduces the indicators for identifying anomalies in fetus and gives basic information about computer-based methods such as traditional image processing, machine learning and deep learning. Then an overview of existing studies which use novel techniques on monitoring fetus health and anomaly detection from ultrasound images is given. Finally, the main challenges of novel techniques and future directions of research on computer-aided monitoring of fetus health are summarized.

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Section: Classification Of Existing Studies According To the Techniqu...mentioning

confidence: 99%

Computer-Aided Monitoring of Fetus Health From Ultrasound Images: A Review

Ataş¹,

Yılmaz²

2022

acin

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“…Sahli et al ( 50 ) proposed a learning framework for the automatic diagnosis of microcephaly and dolichocephaly using intelligent measurements of fetal head. Test results showed that this method could detect and diagnose such abnormalities quickly and accurately, but it focused only on size and shape, excluding internal structures.…”

Section: Ai In the Second And Third Trimester Usmentioning

confidence: 99%

Artificial Intelligence in Prenatal Ultrasound Diagnosis

Wang

Xiu

et al. 2021

Front. Med.

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The application of artificial intelligence (AI) technology to medical imaging has resulted in great breakthroughs. Given the unique position of ultrasound (US) in prenatal screening, the research on AI in prenatal US has practical significance with its application to prenatal US diagnosis improving work efficiency, providing quantitative assessments, standardizing measurements, improving diagnostic accuracy, and automating image quality control. This review provides an overview of recent studies that have applied AI technology to prenatal US diagnosis and explains the challenges encountered in these applications.

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Section: Classification Proceduresmentioning

confidence: 99%

“…Due to its limited important data, ANN is still needed to resolve the nonlinear problem. This method is widely employed for different categorization machine learning [26]. Calculated facial emotive features have been chiefly selected for improving the fear emotion classification, which aids progress in computer-aided security.…”

Section: Classification Proceduresmentioning

confidence: 99%

“…PCA consists in finding a subspace allowing us to classify the new components in order of decreasing importance in order to reduce the dimension of the original space. The transition matrix of the new space is obtained by maximizing the variance between the original variables [25,26]: where S x = Var(X) is the covariance matrix of the original components and is the set of principal axes that define the new space. The following table summarizes the main steps of the PCA Algorithm 1.…”

Section: Classification Proceduresmentioning

confidence: 99%

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Fear Facial Emotion Recognition Based on Angular Deviation

et al. 2021

Self Cite

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This paper shows an advanced method that is able to achieve accurate recognition of fear facial emotions by providing quantitative evaluation of other negative emotions. The proposed approach is focused on both a calibration computing procedure and an important feature pattern technique, which is applied to extract the most relevant characteristics on different human faces. In fact, a 3D/2D projection method is highlighted in order to deal with angular variation (AD) and orientation effects on the emotion detection. Using the combination of the principal component analysis algorithm and the artificial neural network method (PCAN), a supervised classification system is finally achieved to recognize the considered emotion data split into two categories: fear and others. The obtained results have reached an encouraging accuracy up to 20° of AD. Compared to other state-of-art and classification strategies, we recorded the highest accuracy of identified fear emotion. A statistical analysis is carried out on the whole facial emotions, which confirms the best classification performance (positive predictive values (PPV) = 95.13, negative predictive values (NPV) = 94.65, positive likelihood ratio (PLr) = 33.9, and negative likelihood ratio (NLr) = 0.054. The confidence interval for both of PPV and NPV is 92–98%. The proposed framework can be easily applied for any security domain that needs to effectively distinguish the fear cases recognition.

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Supervised classification approach of biometric measures for automatic fetal defect screening in head ultrasound images

Cited by 15 publications

References 15 publications

Computer-Aided Monitoring of Fetus Health From Ultrasound Images: A Review

Computer-Aided Monitoring of Fetus Health From Ultrasound Images: A Review

Artificial Intelligence in Prenatal Ultrasound Diagnosis

Fear Facial Emotion Recognition Based on Angular Deviation

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