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@article{175526,
        author = {Aishwarya G and Gaman N and Kushaja P and Purnima Gowda and Mrs. Divyashree SR},
        title = {Real-Time Detection and Segmentation of Fetal Brain Anomalies},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {3675-3681},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175526},
        abstract = {Fetal brain anomalies are among the most serious congenital disorders, often leading to long-term neurological and developmental challenges. Early and accurate detection is vital for effective prenatal care and intervention. Traditional diagnostic methods rely on manual interpretation of ultrasound and MRI scans, which can be time-consuming and subject to variability. In recent years, deep learning has emerged as a powerful tool in medical image analysis, offering automated, scalable, and often more consistent approaches to anomaly detection. This survey reviews recent advancements in the use of deep learning techniques for detecting fetal brain anomalies, including classification, segmentation, and detection models. Various architectures such as convolutional neural networks, autoencoders, and ensemble models are explored, along with preprocessing techniques, dataset challenges, and evaluation metrics. By comparing the strengths and limitations of different methods, this paper aims to provide a comprehensive overview of the current landscape and identify future directions for research in this critical area of prenatal diagnostics.},
        keywords = {Deep learning, Fetal Brain Anomalies, Medical Image Analysis, Neural Networks},
        month = {April},
        }