Segmentation and classification of fetal spina bifida using DED with FM2DCN

Spina bifida in the foetus is a neurological condition that develops when the spinal cord fails to close properly during pregnancy. A birth defect that causes permanent paralysis in affected babies. Spina bifida is a birth defect that can be treated well if identified and treated early. It is generally agreed that ultrasonic imaging is the gold standard for foetal monitoring. In this research, ultrasound pictures of the foetal spine were segmented and classified into normal and pathological categories using two distinct deep learning algorithms. Three hundred pregnant women were recruited between November 2015 and November 2020 and subjected to three-dimensional ultrasound exams at a hospital. Using an adaptive bilateral filter (ABF), any visible noise in the pictures is eliminated. Segmentation of foetal spina bifida images is performed using the dilated encoder-decoder (DED) technique, and the images are then classified using a Feature Map-based differential convolutional network (FMDDCN). Analysis of segmentation showed that the suggested model was 96% accurate in terms of pixels and 87% accurate in terms of mean intersection over union (MIoU). At the end of the classification evaluation, the suggested model had an accuracy of 96.5, whereas the state-of-the-art methods only managed 95.0.