Abstract: Cerebral palsy is a disorder of neurology that may be caused by prenatal, perinatal or postnatal reasons that result in the failure of motor functioning in children besides mental well-being. Referring to the location of brain injury and the effect of it on the muscle tone, cerebral palsy is classified into subgroups namely spastic, non-spastic etc. Each type of palsy varies in symptoms and hence the therapy planning and rehabilitation are decided depending on the factors involved in each type. This urges the requirement of a suitable technique to classify the type of Palsy at the earlier stages to effectively plan therapy. Functional MRI of the neonatal brain helps in imaging and classification of cerebral palsy. The deep neural network is a subset of machine learning that is widely used in image classification applications. This technique is applied to the functional magnetic resonance brain images of infants to classify the type of cerebral palsy using a deep convolutional network of modified AlexNet architecture that helps the physician further in a planned rehabilitation to facilitate the lifestyle of the affected children.

Pradeepa Palraj and Gopinath Siddan, “Deep Learning Algorithm for Classification of Cerebral Palsy from Functional Magnetic Resonance Imaging (fMRI)” International Journal of Advanced Computer Science and Applications(IJACSA), 12(3), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120383

@article{Palraj2021,
title = {Deep Learning Algorithm for Classification of Cerebral Palsy from Functional Magnetic Resonance Imaging (fMRI)},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120383},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120383},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {3},
author = {Pradeepa Palraj and Gopinath Siddan}
}