Empowering precision medicine: AI-driven schizophrenia diagnosis via EEG signals: A comprehensive review from 2002–2023

M Jafari, D Sadeghi, A Shoeibi, H Alinejad-Rokny… - Applied …, 2024 - Springer
Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional,
and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of …

Deep learning models for diagnosis of schizophrenia using EEG signals: emerging trends, challenges, and prospects

R Ranjan, BC Sahana, AK Bhandari - Archives of Computational Methods …, 2024 - Springer
Schizophrenia (ScZ) is a chronic neuropsychiatric disorder characterized by disruptions in
cognitive, perceptual, social, emotional, and behavioral functions. In the traditional …

SchizoNET: a robust and accurate Margenau–Hill time-frequency distribution based deep neural network model for schizophrenia detection using EEG signals

SK Khare, V Bajaj, UR Acharya - Physiological Measurement, 2023 - iopscience.iop.org
Objective. Schizophrenia (SZ) is a severe chronic illness characterized by delusions,
cognitive dysfunctions, and hallucinations that impact feelings, behaviour, and thinking …

Automatic identification of schizophrenia based on EEG signals using dynamic functional connectivity analysis and 3D convolutional neural network

M Shen, P Wen, B Song, Y Li - Computers in Biology and Medicine, 2023 - Elsevier
Schizophrenia (ScZ) is a devastating mental disorder of the human brain that causes a
serious impact of emotional inclinations, quality of personal and social life and healthcare …

STSN-Net: Simultaneous Tooth Segmentation and Numbering Method in Crowded Environments with Deep Learning

S Wang, S Liang, Q Chang, L Zhang, B Gong, Y Bai… - Diagnostics, 2024 - mdpi.com
Accurate tooth segmentation and numbering are the cornerstones of efficient automatic
dental diagnosis and treatment. In this paper, a multitask learning architecture has been …

A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning

J Rahul, D Sharma, LD Sharma, U Nanda… - Frontiers in Human …, 2024 - frontiersin.org
The electroencephalogram (EEG) serves as an essential tool in exploring brain activity and
holds particular importance in the field of mental health research. This review paper …

An interpretable schizophrenia diagnosis framework using machine learning and explainable artificial intelligence

S Shivaprasad, K Chadaga, CC Dias… - Systems Science & …, 2024 - Taylor & Francis
Schizophrenia is a complicated and multidimensional mental condition marked by a wide
range of emotional, cognitive, and behavioural symptoms. Although the exact root cause of …

CALSczNet: Convolution Neural Network with Attention and LSTM for the Detection of Schizophrenia Using EEG Signals

N Almaghrabi, M Hussain, A Alotaibi - Mathematics, 2024 - mdpi.com
Schizophrenia (SZ) is a serious psychological disorder that affects nearly 1% of the global
population. The progression of SZ disorder causes severe brain damage; its early diagnosis …

Deep Learning based Intelligent Alert System for Visually Impaired People

K Dharavath, V Tejaswi, ESP Kumar… - 2023 International …, 2023 - ieeexplore.ieee.org
According to a study of World Health Organization, there are about 39 million people in the
world who are completely visually impaired. These individuals often face significant …

From Brain Waves to Diagnoses: AI's Role in Schizophrenia Detection

N Shan, V Amsaveni - … on Trends in Engineering Systems and …, 2024 - ieeexplore.ieee.org
Brain signals can be represented as numerical vector sets using electroencephalograms
(EEGs). These signals are used to estimate a wide range of brain disorders, including …