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Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence
The proposed CADS consists of several steps, including dataset, preprocessing, feature extraction, classification, and post-processing.
Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review
Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined.
A Comprehensive Review of Trends, Applications and Challenges In Out-of-Distribution Detection
One of the shortfalls of AI models in general, and deep machine learning in particular, is a drop in performance when faced with shifts in the distribution of data.
Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review
We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities.
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.
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 Schizophrenia in EEG Signals Using CNN-LSTM Models
In this step, the DL models were implemented and compared with different activation functions.
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works
In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction.
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.
An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works
Future works in diagnosing SZ using AI techniques and MRI modalities are recommended in another section.
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.
Material Recognition for Automated Progress Monitoring using Deep Learning Methods
Recent advancements in Artificial intelligence, especially deep learning, has changed many fields irreversibly by introducing state of the art methods for automation.
