Fetal Brain Anomaly Detection via Ultrasound Imaging Using Traditional and Separable CNNs with Xception - Shirsat, Kavita Pankaj; Gawade, Sushopti; Chopade, Swati; Gujare, Rutuja Vikas; Bhandwalkar, Rohint (Journal of Neonatal Surgery, 2025)
Delayed or missed diagnosis of fetal brain anomalies during pregnancy can result in significant developmental challenges and increased rates of newborn death. To address this critical issue, this investigation introduces a diagnostic aid powered by deep learning for the initial detection of these abnormalities through ultrasound imaging, a method recognized for its broad availability and affordability. In this study, we undertook a comparative evaluation of three different convolutional neural network (CNN) architectures, a fundamental CNN framework, a CNN integrated with depthwise separable convolutions, and the Xception model. Our investigation utilized a dataset of 1,786 meticulously labeled ultrasound images, which encompassed 16 varied categories of fetal brain anomalies, including instances of arnold-chiari malformation, ventriculomegaly ranging in severity, and intracranial tumours. After training each model for five epochs, the Xception network demonstrated the highest degree of precision in anomaly classification. Additionally, a graphical user interface (GUI) was developed to enable healthcare professionals to submit ultrasound images and obtain diagnostic predictions accompanied by their respective confidence scores. The outcomes suggest that even with limited training iterations, deep learning models exhibit a notable capability to discern intricate features within fetal ultrasound imagery.