INSIGHTS INTO NEURODEVELOPMENT: AN SVM ALGORITM APPROACH FOR BRAIN AGE PREDICTION IN PRETERM INFANTS FROM NEONATAL MRI
Keywords:
Brain Age Prediction, Preterm Infants, Neonatal MRI, Machine Learning, Support Vector Machine (SVM), NeurodevelopmentAbstract
Accurate brain age prediction in preterm infants is crucial for understanding neurodevelopmental outcomes and identifying
early intervention strategies. This study presents a Support Vector Machine (SVM)-based approach for estimating brain age from neonatal MRI scans, leveraging advanced machine learning techniques to analyze structural and functional brain features.
References
Volpe, J. J. (2019). Brain injury in preterm infants: a complex amalgam of destructive and developmental disturbances. The Lancet Neurology, 18(3), 248-266. [2] Hüppi, P. S., & Dubois, J. (2019). Diffusion tensor imaging of brain development. Seminars in Fetal & Neonatal Medicine, 24(1), 12-18.
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Published
2024-09-10
How to Cite
S. Inderjeet Singh, A.Gayathri, A.Vaishnavi, K. Sai Sanjana. (2024). INSIGHTS INTO NEURODEVELOPMENT: AN SVM ALGORITM APPROACH FOR BRAIN AGE PREDICTION IN PRETERM INFANTS FROM NEONATAL MRI. Journal of Computational Analysis and Applications (JoCAAA), 33(05), 1170–1177. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/1872
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