相关论文: Development and Validation of a Machine Learning A…

Respiratory diseases impose a significant burden on global health, with current diagnostic and management practices primarily reliant on specialist clinical testing. This work aims to develop machine learning-based algorithms to facilitate…

Parkinson's disease (PD) is a highly heterogeneous disease, including which motor symptoms are dominating. Imaging biomarkers that support subtype stratification could also improve biological understanding and study design, and enable…

learning algorithms. In this paper, we review the classification algorithms used in the health care system (chronic diseases) and present the neural network-based Ensemble learning method. We briefly describe the commonly used algorithms…

Background and Aim: Accurate classification of Magnetic Resonance Images (MRI) is essential to accurately predict Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) conversion. Meanwhile, deep learning has been successfully…

The aging society urgently requires scalable methods to monitor cognitive decline and identify social and psychological factors indicative of dementia risk in older adults. Our machine learning (ML) models captured facial, acoustic,…

A comprehensive study on machine and deep learning techniques for classification of normal and abnormal cervical cells by using pap smear images from Herlev dataset results are presented. This dataset includes 917 images and 7 different…

Machine Learning (ML) has emerged as a promising approach in healthcare, outperforming traditional statistical techniques. However, to establish ML as a reliable tool in clinical practice, adherence to best practices regarding data…

This review paper explores recent advances in deep learning approaches for non-invasive cognitive impairment detection. We examine various non-invasive indicators of cognitive decline, including speech and language, facial, and motoric…

A convolutional neural network (CNN) is a deep learning algorithm that has been specifically designed for computer vision applications. The CNNs proved successful in handling the increasing amount of data in many computer vision problems,…

Rehabilitation assessment is critical to determine an adequate intervention for a patient. However, the current practices of assessment mainly rely on therapist's experience, and assessment is infrequently executed due to the limited…

This paper presents a computer-aided cytology diagnosis system designed for animals, focusing on image quality assessment (IQA) using Convolutional Neural Networks (CNNs). The system's building blocks are tailored to seamlessly integrate…

Skin conditions affect an estimated 1.9 billion people worldwide. A shortage of dermatologists causes long wait times and leads patients to seek dermatologic care from general practitioners. However, the diagnostic accuracy of general…

Autism Spectrum Disorder (ASD), which is a neuro development disorder, is often accompanied by sensory issues such an over sensitivity or under sensitivity to sounds and smells or touch. Although its main cause is genetics in nature, early…

Three-dimensional clinical gait analysis is essential for selecting optimal treatment interventions for patients with cerebral palsy (CP), but generates a large amount of time series data. For the automated analysis of these data, machine…

INTRODUCTION: The pharmacological treatment of Major Depressive Disorder (MDD) relies on a trial-and-error approach. We introduce an artificial intelligence (AI) model aiming to personalize treatment and improve outcomes, which was deployed…

A precise assessment of the risk of breast lesions can greatly lower it and assist physicians in choosing the best course of action. To categorise breast lesions, the majority of current computer-aided systems only use characteristics from…

This paper showcases an experimental study on anomaly detection using computer vision. The study focuses on class distinction and performance evaluation, combining OpenCV with deep learning techniques while employing a TensorFlow-based…

Alzheimer's Disease (AD) causes a continuous decline in memory, thinking, and judgment. Traditional diagnoses are usually based on clinical experience, which is limited by some realistic factors. In this paper, we focus on exploiting deep…

In this survey paper, we systematically summarize existing literature on bearing fault diagnostics with machine learning (ML) and data mining techniques. While conventional ML methods, including artificial neural network (ANN), principal…

Epileptic seizure detection and classification in clinical electroencephalogram data still is a challenge, and only low sensitivity with a high rate of false positives has been achieved with commercially available seizure detection tools,…