<![CDATA[Fetal brain abnormalities represent a critical concern in prenatal diagnostics due to their significant impact on neonatal survival and neurological development. Conventional ultrasound (USG) screening relies heavily on expert interpretation, which can be time-consuming and prone to subjectivity. To overcome this constraint, this research develops an automated classification approach employing deep learning techniques to recognize fetal head abnormalities captured through ultrasound scans. The dataset, obtained from a publicly available Kaggle repository, comprises fourteen diagnostic categories, including Arnold Chiari Malformation, Arachnoid Cyst, Cerebellar Hypoplasia, Holoprosencephaly, and Ventriculomegaly variations, among others. Each ultrasound image was subjected to a series of preprocessing operations, such as resizing to 224×224 pixels, applying normalization, and performing data augmentation, to enrich feature variability and strengthen the model’s generalization capability. A pretrained EfficientNet-B3 architecture was fine-tuned for multi-class classification, with the fully connected layer adapted to predict fourteen distinct abnormality classes. Model training was conducted for ten epochs using the Adam optimizer and cross-entropy loss function, with performance evaluated via training loss and validation accuracy metrics. The results demonstrate rapid convergence, with training loss decreasing from 1.7055 in the first epoch to 0.0387 in the final epoch. Concurrently, validation accuracy improved from 79.60% to a peak of 91.37%, indicating strong generalization capability. The consistent upward trend in accuracy and the downward trend in loss confirm the model’s stability and effective learning behavior. Overall, the proposed EfficientNet-B3–based approach achieves high accuracy and robustness, highlighting its potential as an assistive tool for automated prenatal diagnosis of fetal brain abnormalities]]>
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