Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review

Shoeibi, Afshin; Khodatars, Marjane; Jafari, Mahboobeh; Moridian, Parisa; Rezaei, Mitra; Alizadehsani, Roohallah; Khozeimeh, Fahime; Górriz, Juan Manuel; Heras, Jónathan; Panahiazar, Maryam; Nahavandi, Saeid; Acharya, U. Rajendra

doi:10.1016/j.compbiomed.2021.104697

Cited by 118 publications

(49 citation statements)

References 222 publications

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“…To address these challenges, using CADS based on artificial intelligence (AI) can help to improve the speed and accuracy of the epilepsy diagnosis process [11][12][13]. AI-based CADS include ML and DL methods [14][15][16][17]. The most significant difference between CADS based on ML and DL is in the feature extraction step [9].…”

Section: Introductionmentioning

confidence: 99%

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Malekzadeh

Zare

Yaghoobi

et al. 2021

Sensors

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Epilepsy is a brain disorder disease that affects people’s quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5–40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN–RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN–RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN–RNN classification procedure. The results revealed that the proposed CNN–RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively.

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Section: Introductionmentioning

confidence: 99%

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Malekzadeh

Zare

Yaghoobi

et al. 2021

Sensors

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“…Deep Learning is a vast topic that has increased the performance in some classification/prediction problems due to finding complex patterns on highly complex data. Despite being widely used to perform automatic tumor ( 72 ) or multiple sclerosis lesion detection ( 73 ) in brain MR images, it is still not extensively used in mental health disorder detection or risk-estimation of outcomes.…”

Section: Common Machine Learning Algorithmsmentioning

confidence: 99%

Advances in Using MRI to Estimate the Risk of Future Outcomes in Mental Health - Are We Getting There?

Solanes

Raduà

2022

Front. Psychiatry

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“…Furthermore, the authors report a review of studies based on DL networks for diagnosing ASD and the challenges in automatized detection and ASD rehabilitation. Nowadays, there are some DL applications for brain disease diagnoses, such as the ones presented in [39] , which presents a review of automated multiple sclerosis (MS) detection methods based on MRI. They notice that the most used architectures for MS detection are convolutional neural networks (CNNs), autoencoders (AEs), generative adversarial networks (GANs), and CNN-RNN models.…”

Section: Introductionmentioning

confidence: 99%

Attention Measurement of an Autism Spectrum Disorder User Using EEG Signals: A Case Study

Esqueda-Elizondo

Juárez‐Ramírez

López-Bonilla

et al. 2022

MCA

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Autism Spectrum Disorder (ASD) is a neurodevelopmental life condition characterized by problems with social interaction, low verbal and non-verbal communication skills, and repetitive and restricted behavior. People with ASD usually have variable attention levels because they have hypersensitivity and large amounts of environmental information are a problem for them. Attention is a process that occurs at the cognitive level and allows us to orient ourselves towards relevant stimuli, ignoring those that are not, and act accordingly. This paper presents a methodology based on electroencephalographic (EEG) signals for attention measurement in a 13-year-old boy diagnosed with ASD. The EEG signals are acquired with an Epoc+ Brain–Computer Interface (BCI) via the Emotiv Pro platform while developing several learning activities and using Matlab 2019a for signal processing. For this article, we propose to use electrodes F3, F4, P7, and P8. Then, we calculate the band power spectrum density to detect the Theta Relative Power (TRP), Alpha Relative Power (ARP), Beta Relative Power (BRP), Theta–Beta Ratio (TBR), Theta–Alpha Ratio (TAR), and Theta/(Alpha+Beta), which are features related to attention detection and neurofeedback. We train and evaluate several machine learning (ML) models with these features. In this study, the multi-layer perceptron neural network model (MLP-NN) has the best performance, with an AUC of 0.9299, Cohen’s Kappa coefficient of 0.8597, Matthews correlation coefficient of 0.8602, and Hamming loss of 0.0701. These findings make it possible to develop better learning scenarios according to the person’s needs with ASD. Moreover, it makes it possible to obtain quantifiable information on their progress to reinforce the perception of the teacher or therapist.

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Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review

Cited by 118 publications

References 222 publications

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Advances in Using MRI to Estimate the Risk of Future Outcomes in Mental Health - Are We Getting There?

Attention Measurement of an Autism Spectrum Disorder User Using EEG Signals: A Case Study

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