AIMC Topic: Deep Learning

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An open-source deep learning model for predicting effluent concentration in capacitive deionization.

The Science of the total environment
To effectively evaluate the performance of capacitive deionization (CDI), an electrochemical ion separation technology, it is necessary to accurately estimate the number of ions removed (effluent concentration) according to energy consumption. Herein...

Development of non-bias phenotypic drug screening for cardiomyocyte hypertrophy by image segmentation using deep learning.

Biochemical and biophysical research communications
The number of patients with heart failure and related deaths is rapidly increasing worldwide, making it a major problem. Cardiac hypertrophy is a crucial preliminary step in heart failure, but its treatment has not yet been fully successful. In this ...

Real-time monitoring of work-at-height safety hazards in construction sites using drones and deep learning.

Journal of safety research
INTRODUCTION: The construction field is considered one of the most dangerous industries. Accidents and fatalities take place on a daily basis in construction projects. Globally, different levels of government have implemented strict rules and regulat...

Analysis of facial ultrasonography images based on deep learning.

Scientific reports
Transfer learning using a pre-trained model with the ImageNet database is frequently used when obtaining large datasets in the medical imaging field is challenging. We tried to estimate the value of deep learning for facial US images by assessing the...

Real-time driving risk assessment using deep learning with XGBoost.

Accident; analysis and prevention
Traffic crashes typically occur in a few seconds and real-time prediction can significantly benefit traffic safety management and the development of safety countermeasures. This paper presents a novel deep learning model for crash identification base...

Towards an automated approach for smart sterility test examination.

SLAS technology
As new technologies emerge, deep learning applications are often integral parts of new products as features and often as differentiating benefits. This is especially notable in commercial consumer products in everyday applications, such as voice assi...

Dynamic feature learning for COVID-19 segmentation and classification.

Computers in biology and medicine
Since December 2019, coronavirus SARS-CoV-2 (COVID-19) has rapidly developed into a global epidemic, with millions of patients affected worldwide. As part of the diagnostic pathway, computed tomography (CT) scans are used to help patient management. ...

MTRRE-Net: A deep learning model for detection of breast cancer from histopathological images.

Computers in biology and medicine
Histopathological image classification has become one of the most challenging tasks among researchers due to the fine-grained variability of the disease. However, the rapid development of deep learning-based models such as the Convolutional Neural Ne...

A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study.

The Lancet. Digital health
BACKGROUND: There is no simple model to screen for Alzheimer's disease, partly because the diagnosis of Alzheimer's disease itself is complex-typically involving expensive and sometimes invasive tests not commonly available outside highly specialised...

DeepClassPathway: Molecular pathway aware classification using explainable deep learning.

European journal of cancer (Oxford, England : 1990)
OBJECTIVE: HPV-associated head and neck cancer is correlated with favorable prognosis; however, its underlying biology is not fully understood. We propose an explainable convolutional neural network (CNN) classifier, DeepClassPathway, that predicts H...