Tuberculosis

Computer aided detection of tuberculosis using two classifiers.

Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis tools. Tuberculosis can be detected from microscopic slides and chest X-ray but as a result of the high cases of tuberculosis, this method can be tedious for both microbiologist and Radiologist and can lead to miss-diagnosis. The m...

Deep Learning Detection of Active Pulmonary Tuberculosis at Chest Radiography Matched the Clinical Performance of Radiologists.

Background The World Health Organization (WHO) recommends chest radiography to facilitate tuberculosis (TB) screening. However, chest radiograph interpretation expertise remains limited in many regions. Purpose To develop a deep learning system (DLS) to detect active pulmonary TB on chest radiographs and compare its performance to that of radiologists. Materials and Methods A DLS was trained and t...

Tuberculosis Detection from Chest Radiographs: Stop Training Radiologists Now.

Sweat Proteomics in Cystic Fibrosis: Discovering Companion Biomarkers for Precision Medicine and Therapeutic Development.

In clinical routine, the diagnosis of cystic fibrosis (CF) is still challenging regardless of international consensus on diagnosis guidelines and test...

Automatic scoring of COVID-19 severity in X-ray imaging based on a novel deep learning workflow.

In this study, we propose a two-stage workflow used for the segmentation and scoring of lung diseases. The workflow inherits quantification, qualifica...

Machine learning in the loop for tuberculosis diagnosis support.

The use of machine learning (ML) for diagnosis support has advanced in the field of health. In the present paper, the results of studying ML technique...

Simplified Transfer Learning for Chest Radiography Models Using Less Data.

Background Developing deep learning models for radiology requires large data sets and substantial computational resources. Data set size limitations c...

An efficient deep learning-based framework for tuberculosis detection using chest X-ray images.

Early diagnosis of tuberculosis (TB) is an essential and challenging task to prevent disease, decrease mortality risk, and stop transmission to other ...

Classification of COVID-19 from tuberculosis and pneumonia using deep learning techniques.

Deep learning provides the healthcare industry with the ability to analyse data at exceptional speeds without compromising on accuracy. These techniqu...

Discriminating TB lung nodules from early lung cancers using deep learning.

BACKGROUND: In developing countries where both high rates of smoking and endemic tuberculosis (TB) are often present, identification of early lung can...

RA V-Net: deep learning network for automated liver segmentation.

Segmenting liver from CT images is the first step for doctors to diagnose a patient's disease. Processing medical images with deep learning models has...

Novel High-Efficiency Nanocomposite Gate Design of Quantum-Dot Cellular Automata Based on Deep Learning.

With the development of science and technology, the feature size of CMOS devices will always shrink to the limit. Therefore, some new nanodevices will...

A three-methylation-driven gene-based deep learning model for tuberculosis diagnosis in patients with and without human immunodeficiency virus co-infection.

Improved diagnostic tests for tuberculosis (TB) among people with human immunodeficiency virus (HIV) are urgently required. We hypothesized that methy...

Diagnostic accuracy of a commercially available, deep learning-based chest X-ray interpretation software for detecting culture-confirmed pulmonary tuberculosis.

BACKGROUND: Few evaluations of computer-aided detection (CAD) software for analyzing chest radiographs for tuberculosis have used mycobacterial cultur...

Prediction and Diagnosis of Respiratory Disease by Combining Convolutional Neural Network and Bi-directional Long Short-Term Memory Methods.

OBJECTIVE: Based on the respiratory disease big data platform in southern Xinjiang, we established a model that predicted and diagnosed chronic obstru...

Comparison of radiologist versus natural language processing-based image annotations for deep learning system for tuberculosis screening on chest radiographs.

Although natural language processing (NLP) can rapidly extract disease labels from radiology reports to create datasets for deep learning models, this...

Sparse factorization of square matrices with application to neural attention modeling.

Square matrices appear in many machine learning problems and models. Optimization over a large square matrix is expensive in memory and in time. There...

Lightweight YOLOv4 with Multiple Receptive Fields for Detection of Pulmonary Tuberculosis.

The characteristics of pulmonary are complex, and the cost of manual screening is high. The detection model based on convolutional neural network is ...

Tuberculosis Disease Diagnosis Based on an Optimized Machine Learning Model.

Computer science plays an important role in modern dynamic health systems. Given the collaborative nature of the diagnostic process, computer technolo...

Multitask Deep Learning Reconstruction and Localization of Lesions in Limited Angle Diffuse Optical Tomography.

Diffuse optical tomography (DOT) leverages near-infrared light propagation through tissue to assess its optical properties and identify abnormalities....