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Rapid development in Magnetic Resonance Imaging (MRI) has played a key role in prenatal diagnosis over the last few years. Deep learning (DL) architectures can facilitate the process of anomaly detection and affected-organ classification, making diagnosis more accurate and observer-independent. We propose a novel DL image classification architecture, Fetal Organ Anomaly Classification Network (FOAC-Net), which uses squeeze-and-excitation (SE) and naïve inception (NI) modules to automaticallydoi:10.3389/frai.2022.832485 pmid:35372832 pmcid:PMC8972161 fatcat:wdf3upsuwneo3bm3v5ppcvutji