Comprehensive Review on Deep Learning for Neuronal Disorders: Applications of Deep Learning

Vinayak Majhi, Angana Saikia, Amitava Datta, Aseem Sinha and Sudip Paul
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Vinayak Majhi: Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India
Angana Saikia: Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India
Amitava Datta: School of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia
Aseem Sinha: Department of Basic Sciences & Social Sciences, North-Eastern Hill University, Shillong, India
Sudip Paul: Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India & School of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia

International Journal of Natural Computing Research (IJNCR), 2020, vol. 9, issue 1, 27-44

Abstract: In the last few years deep learning (DL) has gained a great attention in modern technology. By using a deep learning method, we can analyse different types of data in different domains which is near to the accuracy of humans. As DL is our upcoming technology and it is also under development, we can say DL is the successor of machine learning (ML) technique. In the present era, ML is used everywhere, wherever we need to analyse statistical data. As we can say DL is our future technology that going to cover every sector of our modern industry, one question always remains: why we are lagging? So, the simple answer in terms of analysing any algorithm is complexity, both time and space. DL needs a large artificial neural network (ANN) with hundreds of hidden layers trained with a huge amount of data. So, to performing these tasks we need high-performance computing device that is very expensive in nowadays. With the growing industries of semiconducting devices, we can easily say that the future of DL is about to come with developing artificial intelligence (AI). As an example, in 2009, the Google Brain, a deep learning artificial intelligence team of Google introduced a Nvidia GPU which increased the learning speed of DL system by 100 times. As of 2017, the intermediate connection of networks increases to a few million units from few thousand, this network can perform several tasks like object recognition, pattern recognition, speech recognition, and image restoration. It has a greater scope in bioengineering since each living organism contains a huge amount data; it can be used for disease diagnosis, rehabilitation, and treatment. It can also help by using data to find the different features and helps us to take several possible decisions in real time. In this review, we found that DL can be very helpful for diagnosing neurological disorders by its symptoms, because DL can be used to identify patterns for each disorder for identification. The benefit is learning how DL can be helpful identifying different neuronal disorders based on different neuropsychiatric symptoms.

Date: 2020
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