ADHD/ADD

Attention-Like Multimodality Fusion With Data Augmentation for Diagnosis of Mental Disorders Using MRI.

The globally rising prevalence of mental disorders leads to shortfalls in timely diagnosis and therapy to reduce patients' suffering. Facing such an urgent public health problem, professional efforts based on symptom criteria are seriously overstretched. Recently, the successful applications of computer-aided diagnosis approaches have provided timely opportunities to relieve the tension in healthc...

DTDO: Driving Training Development Optimization enabled deep learning approach for brain tumour classification using MRI.

A brain tumour is an abnormal mass of tissue. Brain tumours vary in size, from tiny to large. Moreover, they display variations in location, shape, and size, which add complexity to their detection. The accurate delineation of tumour regions poses a challenge due to their irregular boundaries. In this research, these issues are overcome by introducing the DTDO-ZFNet for detection of brain tumour. ...

Cherry on Top or Real Need? A Review of Explainable Machine Learning in Kidney Transplantation.

Research on solid organ transplantation has taken advantage of the substantial acquisition of medical data and the use of artificial intelligence (AI)...

Machine-learning-based feature selection to identify attention-deficit hyperactivity disorder using whole-brain white matter microstructure: A longitudinal study.

BACKGROUND: We aimed to identify important features of white matter microstructures collectively distinguishing individuals with attention-deficit/hyp...

ADHD classification with cross-dataset feature selection for biomarker consistency detection.

Attention deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children. While numerous intelligent methods are applied...

Machine learning based DNA melt curve profiling enables automated novel genotype detection.

Surveillance for genetic variation of microbial pathogens, both within and among species, plays an important role in informing research, diagnostic, p...

Improved clinical data imputation via classical and quantum determinantal point processes.

Imputing data is a critical issue for machine learning practitioners, including in the life sciences domain, where missing clinical data is a typical ...

Dynamic multilayer growth: Parallel vs. sequential approaches.

The decision of when to add a new hidden unit or layer is a fundamental challenge for constructive algorithms. It becomes even more complex in the con...

3D-printed portable device for illicit drug identification based on smartphone-imaging and artificial neural networks.

In this manuscript, a 3D-printed analytical device has been successfully developed to classify illicit drugs using smartphone-based colorimetry. Repre...

Composite attention mechanism network for deep contrastive multi-view clustering.

Contrastive learning-based deep multi-view clustering methods have become a mainstream solution for unlabeled multi-view data. These methods usually u...

Self-Supervised Lightweight Depth Estimation in Endoscopy Combining CNN and Transformer.

In recent years, an increasing number of medical engineering tasks, such as surgical navigation, pre-operative registration, and surgical robotics, re...

Identifying ADHD-Related Abnormal Functional Connectivity with a Graph Convolutional Neural Network.

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder that is characterized by inattention, hyperactivity, and impul...

Autoshaped impulsivity: Some explorations with a neural network model.

This study evaluated the effect of delay and magnitude of reinforcement in Pavlovian contingencies, extending the understanding of the phenomenon of a...

Investigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C prediction.

Hepatitis C, a particularly dangerous form of viral hepatitis caused by hepatitis C virus (HCV) infection, is a major socio-economic and public health...

A multi-featured expression recognition model incorporating attention mechanism and object detection structure for psychological problem diagnosis.

Expression is the main method for judging the emotional state and psychological condition of the human body, and the prediction of changes in facial e...

Enhancing IEP Goal Development for Preschoolers with Autism: A Preliminary Study on ChatGPT Integration.

PURPOSE: The impact of well-crafted IEP goals on student outcomes is well-documented, but creating high-quality goals can be a challenging task for ma...

Deep learning-enabled detection of rare circulating tumor cell clusters in whole blood using label-free, flow cytometry.

Metastatic tumors have poor prognoses for progression-free and overall survival for all cancer patients. Rare circulating tumor cells (CTCs) and rarer...

Sentiment analysis using averaged weighted word vector features.

People use the World Wide Web heavily to share their experiences with entities such as products, services or travel destinations. Texts that provide o...

Structural or/and functional MRI-based machine learning techniques for attention-deficit/hyperactivity disorder diagnosis: A systematic review and meta-analysis.

BACKGROUND: The aim of this study was to investigate the diagnostic value of ML techniques based on sMRI or/and fMRI for ADHD.

Face anti-spoofing with cross-stage relation enhancement and spoof material perception.

Face Anti-Spoofing (FAS) seeks to protect face recognition systems from spoofing attacks, which is applied extensively in scenarios such as access con...