Transfer Learning and Fusion Model for Classification of Epileptic PET Images

Jiang, Huiyan; Gao, Feifei; Duan, Xiaoyu; Bai, Zhiqi; Wang, Zhiguo; Ma, Xiaoqi; Chen, Yen‐Wei

doi:10.1007/978-981-13-8566-7_7

Cited by 7 publications

(14 citation statements)

References 11 publications

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“…Various DL models were developed to detect epileptic seizure using sMRI, fMRI, and PET scans with or without EEG signals [ 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 ]. These models outperformed the conventional models in terms of automatic detection and monitoring of the disease.…”

Section: Non-eeg-based Epileptic Seizures Detectionmentioning

confidence: 99%

“…However, other neuroimaging modalities such as MRI are used for epileptic seizures detection. In [ 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 ], MRI modalities coupled with DL methods have been used to diagnose epileptic seizures. Datasets with non-MRI modalities are not available, and this has led to limited research in this area.…”

Section: Challengesmentioning

confidence: 99%

See 1 more Smart Citation

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Shoeibi

Khodatars

Ghassemi

et al. 2021

IJERPH

289

109

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A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.

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Section: Non-eeg-based Epileptic Seizures Detectionmentioning

confidence: 99%

Section: Challengesmentioning

confidence: 99%

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Shoeibi

Khodatars

Ghassemi

et al. 2021

IJERPH

289

109

View full text Add to dashboard Cite

show abstract

“…T1w MRI gray matter volume [30] cortical thickness, intensity at the grey-white matter contrast, curvature, sulcal depth, intrinsic curvature [37] T2w-MRI volume and intensity sampled on the medial sheet of hippocampus [38] FLAIR-MRI intensity sampled at 25%, 50% and 75% of the cortical thickness and at the grey-white matter boundary [37] DTI MD and FA [39] DKI MK [39] MD, FA, MK and the fusion of FA and MK [40] fMRI fALFF [30] BOLD signal values in different regions, hemispheres and tasks [41] PET hemisphere symmetry tensor [42] Abbrev : MD, Mean diffusion; FA, Fractional Anisotropy; MK, Mean Kurtosis; fALFF, fractional amplitude of low-frequency fluctuation.…”

Section: Modality Features Refmentioning

confidence: 99%

“…Besides the unsupervised end-to-end model, supervised deep neural networks are also capable for hidden feature extraction. As an example, Jiang integrated four sets of features that were extracted from four classic deep neural networks (ResNet -50, VGGNet -16, Inception -V3, SVGG -C3D) and classified the fused feature binarily by a fully convolutional network [42].…”

Section: Hybrid Approachmentioning

confidence: 99%

Machine learning applications on neuroimaging for diagnosis and prognosis of epilepsy: A review

Yuan

Ran

Liu

et al. 2022

Journal of Neuroscience Methods

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“…respectively. Other research has focused on using PET imaging modality for diagnosis (Jiang et al, 2019a;Shiri et al, 2019). ROI, normalization, Ordered subset expectation maximization (OSEM), and down-sampling are some of the PET modality preprocessing methods (Jiang et al, 2019a;Shiri et al, 2019).…”

Section: Medical Imaging Modalities Preprocessingmentioning

confidence: 99%

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

Shoeibi,

Moridian,

Khodatars

et al. 2021

Preprint

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Epileptic seizures are a type of neurological disorder that affect many people worldwide. Specialist physicians and neurologists take advantage of structural and functional neuroimaging modalities to diagnose various types of epileptic seizures. Neuroimaging modalities assist specialist physicians considerably in analyzing brain tissue and the changes made in it. One method to accelerate the accurate and fast diagnosis of epileptic seizures is to employ computer aided diagnosis systems (CADS) based on artificial intelligence (AI) and functional

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Transfer Learning and Fusion Model for Classification of Epileptic PET Images

Cited by 7 publications

References 11 publications

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Machine learning applications on neuroimaging for diagnosis and prognosis of epilepsy: A review

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

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