Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

被引：161
作者：
Noor M.B.T. [1 ]
Zenia N.Z. [1 ]
Kaiser M.S. [1 ]
Mamun S.A. [1 ]
Mahmud M. [2 ]
机构：
[1] Institute of Information Technology, Jahangirnagar University, Savar, Dhaka
[2] Department of Computing & Technology, Nottingham Trent University, Nottingham
来源：
Brain Informatics | 2020年 / 7卷 / 01期
关键词：
Alzheimer’s disease; Machine learning; Neuroimaging; Parkinson’s disease; Schizophrenia;
D O I：
10.1186/s40708-020-00112-2
中图分类号：
学科分类号：
摘要：
Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided. © 2020, The Author(s).
引用
收藏
相关论文
共 98 条
  • [1] Tolosa E., Wenning G., Poewe W., The diagnosis of parkinson’s disease, Lancet Neurol, 5, 1, pp. 75-86, (2006)
  • [2] Danielyan A., Nasrallah H.A., Neurological disorders in schizophrenia, Psychiatric Clinics, 32, 4, pp. 719-757, (2009)
  • [3] Islam J., Zhang Y., Brain mri analysis for alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks, Brain Informatics, 5, pp. 1-14, (2018)
  • [4] Fontana R., Agostini M., Murana E., Mahmud M., Scremin E., Rubega M., Sparacino G., Vassanelli S., Fasolato C., Early hippocampal hyperexcitability in ps2a pp mice: role of mutant ps2 and app, Neurobiol Aging, 50, pp. 64-76, (2017)
  • [5] Leparulo A., Mahmud M., Scremin E., Pozzan T., Vassanelli S., Fasolato C., Dampened slow oscillation connectivity anticipates amyloid deposition in the ps2a pp mouse model of alzheimer’s disease, Cells, 9, 1, (2020)
  • [6] Shatte A., Hutchinson D., Teague S., Machine learning in mental health: A scoping review of methods and applications, Psychol Med, 49, pp. 1-23, (2019)
  • [7] Salvatore C., Et al., Machine learning on brain MRI data for differential diagnosis of Parkinson’s disease and Progressive Supranuclear Palsy, J Neurosci Methods, 222, pp. 230-237, (2014)
  • [8] Mathew N.A., Vivek R.S., Anurenjan P.R., Early diagnosis of alzheimer’s disease from mri images using pnn, Proc. IC4, 161-164, (2018)
  • [9] Mahmud M., Vassanelli S., Processing and analysis of multichannel extracellular neuronal signals: State-of-the-art and challenges, Front Neurosci, (2016)
  • [10] Mahmud M., Vassanelli S., Open-Source Tools for Processing and Analysis of In Vitro Extracellular Neuronal Signals, In Vitro Neuronal Networks: From Culturing Methods to Neuro-Technological Applications, pp. 233-250, (2019)
12345678910