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

Abstract

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed that SZ affects the temporal and anterior lobes of hippocampus regions of the brain. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. Magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder, owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to accurately diagnose SZ. This paper presents a comprehensive overview of studies conducted on the automated diagnosis of SZ using MRI modalities. First, an AI-based computer aided-diagnosis system (CADS) for SZ diagnosis and its relevant sections are presented. Then, this section introduces the most important conventional machine learning (ML) and deep learning (DL) techniques in the diagnosis of diagnosing SZ. A comprehensive comparison is also made between ML and DL studies in the discussion section. In the following, the most important challenges in diagnosing SZ are addressed. Future works in diagnosing SZ using AI techniques and MRI modalities are recommended in another section. Results, conclusion, and research findings are also presented at the end.

References in corpus (30)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Generative Adversarial Networks: An Overview
• Generative Adversarial Network in Medical Imaging: A Review
• Deep Representation Learning with Part Loss for Person Re-Identification
• Epileptic Seizures Detection Using Deep Learning Techniques: A Review
• Generalized Fisher Score for Feature Selection
• Recent Trends in Deep Learning Based Personality Detection
• A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
• Towards Out-Of-Distribution Generalization: A Survey
• Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models
• Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network
• Classification of EEG-Based Brain Connectivity Networks in Schizophrenia Using a Multi-Domain Connectome Convolutional Neural Network
• Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review
• Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies
• A Review on Neural Network Models of Schizophrenia and Autism Spectrum Disorder
• Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review
• PP-LCNet: A Lightweight CPU Convolutional Neural Network
• Dynamic Routing Between Capsules
• CapsNet comparative performance evaluation for image classification
• Multi-Kernel Capsule Network for Schizophrenia Identification
• Deep Neural Generative Model of Functional MRI Images for Psychiatric Disorder Diagnosis
• Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets
• Tensor-Based Fusion of EEG and FMRI to Understand Neurological Changes in Schizophrenia
• Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks
• Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review
• Material Recognition for Automated Progress Monitoring using Deep Learning Methods
• Locally Linear Embedding and fMRI feature selection in psychiatric classification
• Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learning
• Medical Image Harmonization Using Deep Learning Based Canonical Mapping: Toward Robust and Generalizable Learning in Imaging
• Correcting differences in multi-site neuroimaging data using Generative Adversarial Networks

Cited by in corpus (8)

• Epileptic Seizures Detection Using Deep Learning Techniques: A Review
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• Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review
• Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies
• Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review
• Deep Integrated Pipeline of Segmentation Guided Classification of Breast Cancer from Ultrasound Images
• Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression
• Spatial Sequence Attention Network for Schizophrenia Classification from Structural Brain MR Images