2D-DOST for seizure identification from brain MRI during pregnancy using KRVFL

Nayak, Geetanjali; Padhy, Neelamadhab; Mishra, Tusar Kanti

doi:10.1007/s12553-022-00669-4

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…Another commonly implemented method is cross-validation (CV) [348,349], a measure of model robustness. There are various types of cross-validation, including k-fold crossvalidation [350], balanced cross-validation, randomized cross-validation, and leave-one-out cross-validation (LOOCV) [351,352].…”

Section: Training and Evaluationmentioning

confidence: 99%

A Survey of Deep Learning for Alzheimer’s Disease

Zhou

Wang

et al. 2023

MAKE

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Alzheimer’s and related diseases are significant health issues of this era. The interdisciplinary use of deep learning in this field has shown great promise and gathered considerable interest. This paper surveys deep learning literature related to Alzheimer’s disease, mild cognitive impairment, and related diseases from 2010 to early 2023. We identify the major types of unsupervised, supervised, and semi-supervised methods developed for various tasks in this field, including the most recent developments, such as the application of recurrent neural networks, graph-neural networks, and generative models. We also provide a summary of data sources, data processing, training protocols, and evaluation methods as a guide for future deep learning research into Alzheimer’s disease. Although deep learning has shown promising performance across various studies and tasks, it is limited by interpretation and generalization challenges. The survey also provides a brief insight into these challenges and the possible pathways for future studies.

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Section: Training and Evaluationmentioning

confidence: 99%

A Survey of Deep Learning for Alzheimer’s Disease

Zhou

Wang

et al. 2023

MAKE

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“…Thus, the imperative for timely and precise seizure detection during pregnancy looms large, with the potential to avert complications and safeguard both maternal and fetal health. 1,2 Historically, the identification of seizures in pregnant women has rested on two pillars: clinical observations and electroencephalography (EEG). Clinical observations, often carried out by experienced health-care professionals, provide crucial insights into a patient's condition.…”

Section: Introductionmentioning

confidence: 99%

Speeded up robust features trailed GCN for seizure identification during pregnancy

Nayak,

Padhy,

Mishra

2024

JIST

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from the MRI. Second, these features are further mapped to a graph convolutional neural network (GCN). The maximal clique is generated out of these intermediate features and subjected to convolutional neural network (CNN) architecture for classification. The maximal clique acts as an efficient tool for representing final and fine-tuned feature points through combined graph convolution and thus contributes towards efficient classification. The designed framework is validated through benchmark dataset images presented by NITRC. Experimental evaluation is made on samples of 'male', 'female' and 'female with pregnancy'. The overall rate of accuracy stands at 96%, 95%, and 95% respectively.

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2D-DOST for seizure identification from brain MRI during pregnancy using KRVFL

Cited by 2 publications

References 26 publications

A Survey of Deep Learning for Alzheimer’s Disease

A Survey of Deep Learning for Alzheimer’s Disease

Speeded up robust features trailed GCN for seizure identification during pregnancy

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