Navid Ghassemi

Orcid: 0000-0002-6537-0438

According to our database1, Navid Ghassemi authored at least 27 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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In proceedings 
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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Automated detection and forecasting of COVID-19 using deep learning techniques: A review.
Neurocomputing, 2024

Moo-ving Beyond Tradition: Revolutionizing Cattle Behavioural Phenotyping with Pose Estimation Techniques.
CoRR, 2024

2023
Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning.
Appl. Soft Comput., September, 2023

Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review.
Comput. Biol. Medicine, June, 2023

Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review.
Inf. Fusion, May, 2023

2022
Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence.
CoRR, 2022

A Comprehensive Review of Trends, Applications and Challenges In Out-of-Distribution Detection.
CoRR, 2022

Automatic Autism Spectrum Disorder Detection Using Artificial Intelligence Methods with MRI Neuroimaging: A Review.
CoRR, 2022

Automatic Diagnosis of Schizophrenia and Attention Deficit Hyperactivity Disorder in rs-fMRI Modality using Convolutional Autoencoder Model and Interval Type-2 Fuzzy Regression.
CoRR, 2022

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works.
Comput. Biol. Medicine, 2022

An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works.
Comput. Biol. Medicine, 2022

Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies.
Biomed. Signal Process. Control., 2022

Automatic Diagnosis of Schizophrenia in EEG Signals Using Functional Connectivity Features and CNN-LSTM Model.
Proceedings of the Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications, 2022

Automatic Diagnosis of Myocarditis in Cardiac Magnetic Images Using CycleGAN and Deep PreTrained Models.
Proceedings of the Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications, 2022

2021
Medical Images Encryption Based on Adaptive-Robust Multi-Mode Synchronization of Chen Hyper-Chaotic Systems.
Sensors, 2021

Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models.
Frontiers Neuroinformatics, 2021

A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals.
Expert Syst. Appl., 2021

Automatic Diagnosis of Schizophrenia using EEG Signals and CNN-LSTM Models.
CoRR, 2021

Applications of Epileptic Seizures Detection in Neuroimaging Modalities Using Deep Learning Techniques: Methods, Challenges, and Future Works.
CoRR, 2021

Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learning.
CoRR, 2021

An Overview on Artificial Intelligence Techniques for Diagnosis of Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods, Challenges, and Future Works.
CoRR, 2021

Classification of Tuberculosis Type on CT Scans of Lungs using a fusion of 2D and 3D Deep Convolutional Neural Networks.
Proceedings of the Working Notes of CLEF 2021 - Conference and Labs of the Evaluation Forum, Bucharest, Romania, September 21st - to, 2021

2020
Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review.
CoRR, 2020

Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review.
CoRR, 2020

Epileptic seizure detection using deep learning techniques: A Review.
CoRR, 2020

Material Recognition for Automated Progress Monitoring using Deep Learning Methods.
CoRR, 2020

Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images.
Biomed. Signal Process. Control., 2020


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