An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works
A Shoeibi, P Moridian, M Khodatars… - Computers in biology …, 2022 - Elsevier
Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure
include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand …
include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand …
An overview of machine learning methods in enabling IoMT-based epileptic seizure detection
ALN Al-Hajjar, AKM Al-Qurabat - The Journal of Supercomputing, 2023 - Springer
The healthcare industry is rapidly automating, in large part because of the Internet of Things
(IoT). The sector of the IoT devoted to medical research is sometimes called the Internet of …
(IoT). The sector of the IoT devoted to medical research is sometimes called the Internet of …
A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG
Autism spectrum disorder (ASD) is a developmental disability characterized by persistent
impairments in social interaction, speech and nonverbal communication, and restricted or …
impairments in social interaction, speech and nonverbal communication, and restricted or …
Image preprocessing in classification and identification of diabetic eye diseases
Diabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients.
Identifying DED is a crucial activity in retinal fundus images because early diagnosis and …
Identifying DED is a crucial activity in retinal fundus images because early diagnosis and …
Automated detection of COVID-19 through convolutional neural network using chest x-ray images
The COVID-19 epidemic has a catastrophic impact on global well-being and public health.
More than 27 million confirmed cases have been reported worldwide until now. Due to the …
More than 27 million confirmed cases have been reported worldwide until now. Due to the …
A deep learning based framework for diagnosis of mild cognitive impairment
Detecting mild cognitive impairment (MCI) from electroencephalography (EEG) data is a
challenging problem as existing methods rely on machine learning based shallow …
challenging problem as existing methods rely on machine learning based shallow …
Seizure detection algorithm based on improved functional brain network structure feature extraction
L Jiang, J He, H Pan, D Wu, T Jiang, J Liu - Biomedical Signal Processing …, 2023 - Elsevier
Epilepsy is one of the most common neurological disorders. Accurate detection of epileptic
seizures is essential for treatment. A seizure detection method with the structure of functional …
seizures is essential for treatment. A seizure detection method with the structure of functional …
Classification of normal and depressed EEG signals based on centered correntropy of rhythms in empirical wavelet transform domain
A widespread brain disorder of present days is depression which influences 264 million of
the world's population. Depression may cause diverse undesirable consequences, including …
the world's population. Depression may cause diverse undesirable consequences, including …
Automatic breast lesion segmentation in phase preserved DCE-MRIs
We offer a framework for automatically and accurately segmenting breast lesions from
Dynamic Contrast Enhanced (DCE) MRI in this paper. The framework is built using max flow …
Dynamic Contrast Enhanced (DCE) MRI in this paper. The framework is built using max flow …
Patient assignment optimization in cloud healthcare systems: a distributed genetic algorithm
Integrating Internet technologies with traditional healthcare systems has enabled the
emergence of cloud healthcare systems. These systems aim to optimize the balance …
emergence of cloud healthcare systems. These systems aim to optimize the balance …