Survey of explainable AI techniques in healthcare
Artificial intelligence (AI) with deep learning models has been widely applied in numerous
domains, including medical imaging and healthcare tasks. In the medical field, any judgment …
domains, including medical imaging and healthcare tasks. In the medical field, any judgment …
[HTML][HTML] Application of data fusion for automated detection of children with developmental and mental disorders: A systematic review of the last decade
Mental health is a basic need for a sustainable and developing society. The prevalence and
financial burden of mental illness have increased globally, and especially in response to …
financial burden of mental illness have increased globally, and especially in response to …
Emotion recognition in EEG signals using deep learning methods: A review
Emotions are a critical aspect of daily life and serve a crucial role in human decision-making,
planning, reasoning, and other mental states. As a result, they are considered a significant …
planning, reasoning, and other mental states. As a result, they are considered a significant …
Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review
Brain diseases, including tumors and mental and neurological disorders, seriously threaten
the health and well-being of millions of people worldwide. Structural and functional …
the health and well-being of millions of people worldwide. Structural and functional …
Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review
In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of
mortality globally. At early stages, CVDs appear with minor symptoms and progressively get …
mortality globally. At early stages, CVDs appear with minor symptoms and progressively get …
Evaluation of autism spectrum disorder based on the healthcare by using artificial intelligence strategies
The behaviors of children with autism spectrum disorder (ASD) are often erratic and difficult
to predict. Most of the time, they are unable to communicate effectively in their own …
to predict. Most of the time, they are unable to communicate effectively in their own …
Empowering precision medicine: AI-driven schizophrenia diagnosis via EEG signals: A comprehensive review from 2002–2023
Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional,
and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of …
and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of …
Role of artificial intelligence for autism diagnosis using DTI and fMRI: A survey
Autism spectrum disorder (ASD) is a wide range of diseases characterized by difficulties with
social skills, repetitive activities, speech, and nonverbal communication. The Centers for …
social skills, repetitive activities, speech, and nonverbal communication. The Centers for …
[HTML][HTML] ALEC: active learning with ensemble of classifiers for clinical diagnosis of coronary artery disease
Invasive angiography is the reference standard for coronary artery disease (CAD) diagnosis
but is expensive and associated with certain risks. Machine learning (ML) using clinical and …
but is expensive and associated with certain risks. Machine learning (ML) using clinical and …
Automatic diagnosis of myocarditis disease in cardiac MRI modality using deep transformers and explainable artificial intelligence
Myocarditis is a significant cardiovascular disease (CVD) that poses a threat to the health of
many individuals by causing damage to the myocardium. The occurrence of microbes and …
many individuals by causing damage to the myocardium. The occurrence of microbes and …