Foetal neurodegenerative disease classification using improved deep ResNet classification based VGG-19 feature extraction network

Neurodegenerative disease defined about death of some brain parts. This disease is the dangerous disease to cure with devastating results. In addition to the elderly, the neurodegenerative disease threatening the pregnant women since it affects the foetus. The existing studies related with neurodegenerative disease is very few. The diagnosis of the neurodegenerative disease occur in foetal during pregnancy is the major challenge in medical field. In this proposed study, the neurodegenerative disease for foetus are classified based on the novel VGG-19 feature extraction and improved deep ResNet classifier. This proposed concept follows feature extraction and transfer learning based classification. For effective feature extraction, the Visual Geometry Group -VGG-19 has been widely used and for better classification of the neurodegenerative disease in foetal MRI brain the proposed improved Deep Residual Network-ResNet classifier is implemented. ResNet allows the alternate shortcut path and reduces the vanishing gradient problem. If the current layer is not required, CNN weight layer is bypassed by ResNet identity mapping. The training set over fitting problem is thus avoided. The VGG-19 network major role is to increase the CNN depth with the help of 3 × 3 filter size. The proposed study are evaluated in terms of various performance metrics and several activation function compared with proposed swish activation function. The results shows that the proposed method resulted in better values compared with existing studies used the foetal brain images.