Intelligent detection of fetal hydrocephalus

This paper describes an enhanced process able to attain righteous classification of morphological malformation in foetal head ultrasound images. These anomalies can be detected approximately 20-22 weeks. In effect, the experts rely on manual analysis by extracting typical biometric measures from hea...

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Bibliographic Details
Published in:Computer methods in biomechanics and biomedical engineering. 2020-11, Vol.8 (6), p.641-648
Main Authors: Sahli, Hanene, Sayadi, Mounir, Rachdi, Radhouane
Format: Article
Language:English
Subjects:
classification
conventional neural network
foetal US images
Hydrocephalus
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Summary:This paper describes an enhanced process able to attain righteous classification of morphological malformation in foetal head ultrasound images. These anomalies can be detected approximately 20-22 weeks. In effect, the experts rely on manual analysis by extracting typical biometric measures from head region to interpret pregnancy evolution. The contribution of this work presents a totally computerised method of cerebral defect-recognition based on conventional neural network (CNN) in order to supply quantitative appraisal of hydrocephalus (HD) or healthy (HL) topics. The obtained results have achieved an important classification (Accuracy = 97.20%, sensitivity = 97.95% and specificity = 98.23%) when applying the CNN. The proposed methodology enables us to assess the anomalous cases in premature period within a reduced processing time. Experimental results prove the success of the proposed rating system for a suitable diagnostic of foetal head hydrocephalus when compared to previous work.
ISSN:2168-1163
2168-1171
DOI:10.1080/21681163.2020.1780156