MSAG_ENET Based Medical Image Augmentation and Classification of 2D-US Fetal Brain Anomalies.

Medical imaging is essential to modern life support. Different approaches and standards exist for detecting life-threatening disorders. However, manually detecting anomalies takes time and is prone to error. Additionally, it demands great expertise and competence. Deep learning in medical imaging improves accuracy and provides several benefits. Privacy concerns limit access to medical data, especially images. This problem can be solved with data augmentation. Image quality determines prediction accuracy. Preprocessing images improves quality. This article addresses data augmentation and image enhancement with a multiscale self-attention generator. Foundational network is Inception. Elastic_net used for selection and regularisation of features. The anisotropic diffusion filter used for reducing speckle noise in the model. The ZONODO foetal planes dataset is used as input. The proposed model produces better results for classification as accuracy is 98.7, precision is 97.5, recall is 97.8 and F1-Score is 97.5 when compared with existing methods. [ABSTRACT FROM AUTHOR]