Automatic Neuro Disease Classification Based on Gait Analysis using Bi-stacked GRU
Main Article Content
Abstract
Deep learning is a branch of machine learning based on deep neural network used to train the computers without being explicitly programmed. Recurrent neural network (RNN) is a part of deep learning methods which is the first algorithm in deep learning that produce output based on the sequence of input. RNN have multiple advantages in the field of medicine to solve it. Long short term memory (LSTM) an extension of RNN solves vanishing gradient and exploding problem in RNN by using to store the long sequence of memory through cell state. Neurodegenerative disease affects the neurons in Human brain which are the blocks of nervous system includes brain and spinal cord, if it is die or damaged can’t be replaced and motion of two lower limbs causing gait disorder. Such diseases are treated with LSTM model, but accurate results are not able to achieve due to gradient exploding problem. To improve the accuracy we proposed the variants of Gated
Downloads
Metrics
Article Details
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.