AIMC Topic: Neural Networks, Computer

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Consistency of convolutional neural networks in dermoscopic melanoma recognition: A prospective real-world study about the pitfalls of augmented intelligence.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: Deep-learning convolutional neural networks (CNNs) have outperformed even experienced dermatologists in dermoscopic melanoma detection under controlled conditions. It remains unexplored how real-world dermoscopic image transformations aff...

Improved bioimpedance spectroscopy tissue classification through data augmentation from generative adversarial networks.

Medical & biological engineering & computing
Bioimpedance spectroscopy is a tissue classification technique with many clinical applications. Similarly to other data-driven methods, it requires large amounts of data to accurately distinguish similar classes of tissue. Classifiers trained on smal...

Pre-Processing techniques and artificial intelligence algorithms for electrocardiogram (ECG) signals analysis: A comprehensive review.

Computers in biology and medicine
Electrocardiogram (ECG) are the physiological signals and a standard test to measure the heart's electrical activity that depicts the movement of cardiac muscles. A review study has been conducted on ECG signals analysis with the help of artificial i...

An effective framework for predicting drug-drug interactions based on molecular substructures and knowledge graph neural network.

Computers in biology and medicine
Drug-drug interactions (DDIs) play a central role in drug research, as the simultaneous administration of multiple drugs can have harmful or beneficial effects. Harmful interactions lead to adverse reactions, some of which can be life-threatening, wh...

Machine learning (ML) techniques as effective methods for evaluating hair and skin assessments: A systematic review.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Machine Learning (ML) techniques provide the ability to effectively evaluate and analyze human skin and hair assessments. The aim of this study is to systematically review the effectiveness of applying Machine Learning (ML) methods and Artificial Int...

Developing a Machine Learning Algorithm to Predict the Probability of Medical Staff Work Mode Using Human-Smartphone Interaction Patterns: Algorithm Development and Validation Study.

Journal of medical Internet research
BACKGROUND: Traditional methods for investigating work hours rely on an employee's physical presence at the worksite. However, accurately identifying break times at the worksite and distinguishing remote work outside the worksite poses challenges in ...

Optimization-enabled deep learning model for disease detection in IoT platform.

Network (Bristol, England)
Nowadays, Internet of things (IoT) and IoT platforms are extensively utilized in several healthcare applications. The IoT devices produce a huge amount of data in healthcare field that can be inspected on an IoT platform. In this paper, a novel algor...

Microscopic urinary particle detection by different YOLOv5 models with evolutionary genetic algorithm based hyperparameter optimization.

Computers in biology and medicine
The diagnosis of kidney disease often involves analysing urine sediment particles. Traditionally, urinalysis was performed manually by collecting urine samples and using a centrifuge, which was prone to manual errors and relied on labour-intensive pr...

Quantitative gait analysis and prediction using artificial intelligence for patients with gait disorders.

Scientific reports
Quantitative Gait Analysis (QGA) is considered as an objective measure of gait performance. In this study, we aim at designing an artificial intelligence that can efficiently predict the progression of gait quality using kinematic data obtained from ...

MSCT-UNET: multi-scale contrastive transformer within U-shaped network for medical image segmentation.

Physics in medicine and biology
Automatic mutli-organ segmentation from anotomical images is essential in disease diagnosis and treatment planning. The U-shaped neural network with encoder-decoder has achieved great success in various segmentation tasks. However, a pure convolution...