similiarity_Deep Learning for Improving the Effectiveness of Routine Prenatal Screening for Major Congenital Heart Diseases

firdaus, firdaus (2022) similiarity_Deep Learning for Improving the Effectiveness of Routine Prenatal Screening for Major Congenital Heart Diseases. Turnitin Universitas Sriwijaya.

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Abstract

Early prenatal screening with an ultrasound (US) can significantly lower newborn mortality caused by congenital heart diseases (CHDs). However, the need for expertise in fetal cardiologists and the high volume of screening cases limit the practically achievable detection rates. Hence, automated prenatal screening to support clinicians is desirable. This paper presents and analyses potential deep learning (DL) techniques to diagnose CHDs in fetal USs. Four convolutional neural network architectures were compared to select the best classifier with satisfactory results. Hence, dense convolutional network (DenseNet) 201 architecture was selected for the classification of seven CHDs, such as ventricular septal defect, atrial septal defect, atrioventricular septal defect, Ebstein’s anomaly, tetralogy of Fallot, transposition of great arteries, hypoplastic left heart syndrome, and a normal control. The sensitivity, specificity, and accuracy of the DenseNet201 model were 100%, 100%, and 100%, respectively, for the intra-patient scenario and 99%, 97%, and 98%, respectively, for the inter-patient scenario. We used the intra-patient DL prediction model to validate our proposed model against the prediction results of three expert fetal cardiologists. The proposed model produces a satisfactory result, which means that our model can support expert fetal cardiologists to interpret the decision to improve CHD diagnostics. This work represents a step toward the goal of assisting front-line sonographers with CHD diagnoses at the population level. © 2022 by the authors.

Item Type:	Other
Subjects:	#3 Repository of Lecturer Academic Credit Systems (TPAK) > Results of Ithenticate Plagiarism and Similarity Checker
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Mr Firdaus Firdaus
Date Deposited:	17 Mar 2023 13:27
Last Modified:	17 Mar 2023 13:27
URI:	http://repository.unsri.ac.id/id/eprint/90893

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