AIMC Topic: Deep Learning

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Characterizing Macrophages Diversity in COVID-19 Patients Using Deep Learning.

Genes
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent responsible for coronavirus disease 2019 (COVID-19), has affected the lives of billions and killed millions of infected people. This virus has been demonstrated t...

Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs.

JAMA network open
IMPORTANCE: Early detection of pneumothorax, most often via chest radiography, can help determine need for emergent clinical intervention. The ability to accurately detect and rapidly triage pneumothorax with an artificial intelligence (AI) model cou...

An AVMD-DBN-ELM Model for Bearing Fault Diagnosis.

Sensors (Basel, Switzerland)
Rotating machinery often works under complex and variable working conditions; the vibration signals that are widely used for the health monitoring of rotating machinery show extremely complicated dynamic frequency characteristics. It is unlikely that...

Deep Learning Based Infrared Thermal Image Analysis of Complex Pavement Defect Conditions Considering Seasonal Effect.

Sensors (Basel, Switzerland)
Deep learning techniques underpinned by extensive data sources encompassing complex pavement features have proven effective in early pavement damage detection. With pavement features exhibiting temperature variation, inexpensive infra-red imaging tec...

A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques.

Sensors (Basel, Switzerland)
In this review, we provide a detailed coverage of multi-sensor fusion techniques that use RGB stereo images and a sparse LiDAR-projected depth map as input data to output a dense depth map prediction. We cover state-of-the-art fusion techniques which...

Evaluation of roadside air quality using deep learning models after the application of the diesel vehicle policy (Euro 6).

Scientific reports
Euro 6 is the latest vehicle emission standards for pollutants such as CO, NO and PM, that all new vehicles must comply, and it was introduced in September 2015 in South Korea. This study examined the effect of Euro 6 by comparing the measured pollut...

Ensemble deep learning enhanced with self-attention for predicting immunotherapeutic responses to cancers.

Frontiers in immunology
INTRODUCTION: Despite the many benefits immunotherapy has brought to patients with different cancers, its clinical applications and improvements are still hindered by drug resistance. Fostering a reliable approach to identifying sufferers who are sen...

Efficient framework for brain tumor detection using different deep learning techniques.

Frontiers in public health
The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are classified using a biopsy, which is normally performed after the final brain surgery. Deep learning technology advancements have assisted the health professionals...

Passive tracking of underwater acoustic targets based on multi-beam LOFAR and deep learning.

PloS one
Conventional passive tracking methods for underwater acoustic targets in sonar engineering generate time azimuth histogram and use it as a basis for target azimuth and tracking. Passive underwater acoustic targets only have azimuth information on the...

A CT-Based Deep Learning Radiomics Nomogram to Predict Histological Grades of Head and Neck Squamous Cell Carcinoma.

Academic radiology
RATIONALE AND OBJECTIVES: Accurate pretreatment assessment of histological differentiation grade of head and neck squamous cell carcinoma (HNSCC) is crucial for prognosis evaluation. This study aimed to construct and validate a contrast-enhanced comp...