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Tuberculosis

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The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China.

PloS one
BACKGROUND AND OBJECTIVE: Tuberculosis (Tuberculosis, TB) is a public health problem in China, which not only endangers the population's health but also affects economic and social development. It requires an accurate prediction analysis to help to m...

First-line drug resistance profiling of : a machine learning approach.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The persistence and emergence of new multi-drug resistant Mycobacterium tuberculosis (M. tb) strains continues to advance the devastating tuberculosis (TB) epidemic. Robust systems are needed to accurately and rapidly perform drug-resistance profilin...

E-TBNet: Light Deep Neural Network for Automatic Detection of Tuberculosis with X-ray DR Imaging.

Sensors (Basel, Switzerland)
Currently, the tuberculosis (TB) detection model based on chest X-ray images has the problem of excessive reliance on hardware computing resources, high equipment performance requirements, and being harder to deploy in low-cost personal computer and ...

TBNet: a context-aware graph network for tuberculosis diagnosis.

Computer methods and programs in biomedicine
Tuberculosis (TB) is an infectious bacterial disease. It can affect the human lungs, brain, bones, and kidneys. Pulmonary tuberculosis is the most common. This airborne bacterium can be transmitted with the droplets by coughing and sneezing. So far, ...

Deep learning aided quantitative analysis of anti-tuberculosis fixed-dose combinatorial formulation by terahertz spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Anti-tuberculosis fixed-dose combinatorial formulation (FDCs) is an effective drug for the treatment of tuberculosis. As a compound medicine, its efficacy is based on the comprehensive action of multiple main ingredients. If the content of an active ...

Ensemble of EfficientNets for the Diagnosis of Tuberculosis.

Computational intelligence and neuroscience
Tuberculosis (TB) remains a life-threatening disease and is one of the leading causes of mortality in developing regions due to poverty and inadequate medical resources. Tuberculosis is medicable, but it necessitates early diagnosis through reliable ...

A Novel and Robust Approach to Detect Tuberculosis Using Transfer Learning.

Journal of healthcare engineering
Deep learning has emerged as a promising technique for a variety of elements of infectious disease monitoring and detection, including . We built a deep convolutional neural network (CNN) model to assess the generalizability of the deep learning mode...

An accurate artificial intelligence system for the detection of pulmonary and extra pulmonary Tuberculosis.

Tuberculosis (Edinburgh, Scotland)
Tuberculosis (TB) is the greatest irresistible illness in humans, caused by microbes Mycobacterium TB (MTB) bacteria and is an infectious disease that spreads from one individual to another through the air. It principally influences lung, which is te...

DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The global pandemic of coronavirus disease 2019 (COVID-19) is continuing to have a significant effect on the well-being of the global population, thus increasing the demand for rapid testing, diagnosis, and treatment. As COVID-19 can cause severe pne...

Automated machine learning for endemic active tuberculosis prediction from multiplex serological data.

Scientific reports
Serological diagnosis of active tuberculosis (TB) is enhanced by detection of multiple antibodies due to variable immune responses among patients. Clinical interpretation of these complex datasets requires development of suitable algorithms, a time c...