Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Álvarez JD, Matias-Guiu JA, Cabrera-Martín MN, Risco-Martín JL, Ayala JL (2019) An application of machine learning with feature selection to improve diagnosis and classification of neurodegenerative disorders. BMC Bioinform 20:491
Attallah O, Sharkas MA, Gadelkarim H (2019) Fetal brain abnormality classification from MRI images of different gestational age. Brain Sci 9:231
Attallah O, Sharkas MA, Gadelkarim H (2020) Deep learning techniques for automatic detection of embryonic neurodevelopmental disorders. Diagnostics 10:27
Benussi A, Grassi M, Palluzzi F, Koch G, Di Lazzaro V, Nardone R et al (2020) Classification accuracy of transcranial magnetic stimulation for the diagnosis of neurodegenerative dementias. Ann Neurol 87:394–404
Beyrami SMG, Ghaderyan P (2020) A robust, cost-effective and non-invasive computer-aided method for diagnosis three types of neurodegenerative diseases with gait signal analysis. Measurement 156:107579
Farid AA, Selim G, Khater H (2020) Applying artificial intelligence techniques for prediction of neurodegenerative disorders: a comparative case-study on clinical tests and neuroimaging tests with Alzheimer’s disease.
Gopal SSAG, Dessai MA (2019) Automatic classification of cervical magnetic resonance images using ResNet-101.
Lei B, Zhao Y, Huang Z, Hao X, Zhou F, Elazab A, et al (2020) Adaptive sparse learning using multi-template for neurodegenerative disease diagnosis. Med Image Anal 61:101632
McKay R (2004) Stem cell biology and neurodegenerative disease. Philos Tran R Soc Lond B 359:851–856
Myszczynska MA, Ojamies PN, Lacoste AM, Neil D, Saffari A, Mead R et al (2020) Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat Rev Neurol 16:440–456
Nalivaeva NN, Turner AJ, Zhuravin IA (2018) Role of prenatal hypoxia in brain development, cognitive functions, and neurodegeneration. Front Neurosci 12:825
Naseer A, Rani M, Naz S, Razzak MI, Imran M, Xu G (2020) Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Comput Appl 32:839–854
Patani R, Lewis PA, Trabzuni D, Puddifoot CA, Wyllie DJ, Walker R et al (2012) Investigating the utility of human embryonic stem cell-derived neurons to model ageing and neurodegenerative disease using whole-genome gene expression and splicing analysis. J Neurochem 122:738–751
Plisson F, Piggott AM (2019) Predicting blood–brain barrier permeability of marine-derived kinase inhibitors using ensemble classifiers reveals potential hits for neurodegenerative disorders. Mar Drugs 17:81
Segovia F, Górriz J, Ramírez J, Martínez-Murcia FJ, García-Pérez M (2018) Using deep neural networks along with dimensionality reduction techniques to assist the diagnosis of neurodegenerative disorders. Logic J IGPL 26:618–628
Shah SAA, Habib N, Aziz W, Khan EU, Nadeem MSA (2020) Classification of control and neurodegenerative disease subjects using tree based classifiers. J Pharm Res Int 63–73
Talo M, Yildirim O, Baloglu UB, Aydin G, Acharya UR (2019) Convolutional neural networks for multi-class brain disease detection using MRI images. Comput Med Imaging Graph 78:101673
Vatathanavaro S, Tungjitnob S, Pasupa K. White blood cell classification: a comparison between VGG-19 and ResNet-50 Models.
Yu Y, Lin H, Meng J, Wei X, Guo H, Zhao Z (2017) Deep transfer learning for modality classification of medical images. Information 8:91
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Siddan, G., Palraj, P. Foetal neurodegenerative disease classification using improved deep ResNet classification based VGG-19 feature extraction network. Multimed Tools Appl 81, 2393–2408 (2022). https://doi.org/10.1007/s11042-021-11543-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-021-11543-2