Towards Detection of Congenital Birth Defect in New born Using Autoencoder Neural Network

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dc.contributor.authorBabatunde Ronke Seyi
dc.date.accessioned2024-07-19T16:52:07Z
dc.date.available2024-07-19T16:52:07Z
dc.date.issued2023-04-12
dc.description.abstractCongenital birth defects are developmental issues occurring during fetal development. Such defects vary in severity and can affect structure, function, and metabolism of a fetus. Common examples include heart defects, cleft lip, and Down syndrome. Risk factors include maternal age, infections, medications, and genetics. Detecting these defects is crucial, given their impact on infant mortality. Medical imaging techniques allow healthcare professionals to visualize these defects and assess their severity, aiding in treatment planning and management. There has been several attempts to detecting birth defects using artificial intelligence and machine learning, some of which have yielded positive outcomes but are limited in terms of identifying and effectively predicting the defect, complexity in model interpretation and timely detection. These calls for continual research in this domain. This work implements an Autoencoder model for birth abnormality detection. The process involves data acquisition, preprocessing, feature extraction, model training and evaluation. Model evaluation was based on qualitative analysis and visualization. The experimental result shows the effectiveness and subsequent practicability of the Autoencoder model.
dc.identifier.citationBabatunde et. al., 2023
dc.identifier.urihttp://aujet.adelekeuniversity.edu.ng/index.php/aujet/article/view/351
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/1673
dc.language.isoen
dc.publisherPublished by Faculty of Engineering and Technology, Adeleke University. Ede.
dc.titleTowards Detection of Congenital Birth Defect in New born Using Autoencoder Neural Network
dc.typeArticle
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