A Hybrid <scp>CNN</scp> ‐Transformer Model with Crossover Boosted Cheetah Optimization for Prenatal Spina Bifida Identification from Ultrasound Images

OBJECTIVES: Spina bifida is a birth defect caused by the incomplete closure of the neural tube around the spinal cord. Earlier, various deep learning-based models were developed, but they failed to capture the fine details from the ultrasound images that reduce the detection accuracy. Hence, to overcome these challenges, an automated and trustworthy deep learning framework is significant for accurate identification of spina bifida from second-trimester fetal ultrasound images. METHODS: This research proposes a novel hybrid convolutional neural network-transformer model for spina bifida identification, where convolutional neural network integration enables the model to extract local features and transformer encoder integration enables the model to capture global contextual dependencies. Further, spatial feature representation is enhanced by using the multi-resolution convolutional neural network blocks and positional encoding. On the other hand, clinically relevant regions are highlighted through the use of a self-attention mechanism within the proposed model. The hyperparameters of the proposed model are fine-tuned using a crossover boosted cheetah optimization algorithm, which integrates the strengths of the Cheetah optimizer and the Crossover Mechanism. Thereby, optimizing the performance of the proposed model and making the model robust to spina bifida identification. RESULTS: The proposed model achieves robust diagnostic performance, which attains an accuracy of 99.02%, an F1-score of 98.21%, and a lower inference time of 0.88 second, demonstrating its suitability in real-time prenatal ultrasound screening applications. CONCLUSIONS: The proposed model provides an efficient and clinically viable solution for automated prenatal spina bifida detection.