Abstract: Down syndrome is a genetic disorder caused by the presence of an extra copy of chromosome 21, affecting both neurological development and physical features. Early and accurate diagnosis is critical for ensuring timely medical intervention and support. This study presents a comparative analysis of prenatal (ultrasound) and postnatal (facial) imaging modalities for the detection of Down syndrome using deep learning techniques. We employed VGG19, ResNet50, DenseNet121, MobileNetV2, and the Vision Transformer for image classification. An ensemble model integrating four CNN architectures achieved superior performance, with 92% test accuracy on prenatal images and 83%on postnatal images. Among the individual models, ResNet50 out-performed the others across both modalities. Evaluation metrics, including accuracy, precision, recall, and F1-score, confirm the effectiveness of the proposed framework. These results highlight the potential of ensemble learning to enhance the early detection of Down syndrome and improve accessibility to healthcare.

Labanti Singha and Iqbal Ahmed, “Comparative Study of Prenatal and Postnatal Images for Detecting Down Syndrome in Children” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160688

@article{Singha2025,
title = {Comparative Study of Prenatal and Postnatal Images for Detecting Down Syndrome in Children},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160688},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160688},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Labanti Singha and Iqbal Ahmed}
}