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Found 9 papers, 0 papers with code
Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression
So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians.
Accurate Prediction Using Triangular Type-2 Fuzzy Linear Regression
The current survey proposes a triangular type-2 fuzzy regression (TT2FR) model to ameliorate the efficiency of the model by handling the uncertainty in the data.
Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies
The tunable-Q wavelet transform (TQWT) is employed to decompose the EEG signals into different sub-bands.
Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learning
Also, in order to evaluate the method, a dataset containing 3163 images from 189 patients has been collected and labeled by physicians.
Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images
In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images.
Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991-2020)
We have little knowledge about the optimal treatment methods as there are many sources of uncertainty in medical science.
Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review
Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL is presented.
Epileptic Seizures Detection Using Deep Learning Techniques: A Review
The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed.
Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review
Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging.
