Abnormality Detection in Chest X-ray Images Using Uncertainty Prediction Algorithms

2021 ◽  
Author(s):  
Nurbek Saparkhojayev ◽  
Lazzat Zholayeva ◽  
Yerzhan Tashkenbayev ◽  
Dinara Tokseit
Keyword(s):  
Abnormality Detection ◽  
X Ray ◽  
Uncertainty Prediction ◽  
Prediction Algorithms ◽  
Chest X Ray

scholarly journals ChestXthon: An algorithm for Abnormality Detection in Chest Radiographs

2018 ◽  
Vol 7 (4) ◽  
pp. 2528
Author(s):  
N. Sarada ◽  
K. Thirupathi Rao ◽  
K. V. Ramana
Keyword(s):  
Heart Failure ◽  
Lung Tumor ◽  
Chest Radiographs ◽  
Abnormality Detection ◽  
X Ray ◽  
Lung Tuberculosis ◽  
Chest X Ray ◽  
Critical Consideration

Chest illnesses like heart failure, lung tumor or lung tuberculosis, and so on is frequently in view of chest X-ray images (CXR). The ailments are treatable on the off chance that they are recognized in their beginning times. Analyzing CXR is a tedious procedure. Now and again, therapeutic specialists had ignored the illnesses in their first examinations on CXR, and when the pictures were reevaluated, the malady signs could be detected. Furthermore, the quantity of CXR to look at is various and a long ways past the capacity of accessible therapeutic staff, particularly in creating nations. A PC supported finding (CAD) framework can check presumed zones on CXR for cautious examination by restorative specialists, and can give caution in the cases that need critical consideration. This paper reports our persistent work on developing an algorithm that aids the radiologists for the diagnosis of chest radiographs.  

scholarly journals Review on Deep Learning Methods for Chest X-Ray based Abnormality Detection and Thoracic Pathology Classification

2021 ◽  
Vol 7 (4) ◽  
pp. 16-32
Author(s):  
Joana Rocha ◽  
Ana Maria Mendonça ◽  
Aurélio Campilho
Keyword(s):  
Deep Learning ◽  
Learning Strategies ◽  
Abnormality Detection ◽  
X Ray ◽  
Pathology Classification ◽  
Chest X Ray ◽  
Increasing Demand ◽  
Computational Resources ◽  
Relevant Work ◽  
Aided Diagnosis

Backed by more powerful computational resources and optimized training routines, deep learning models have proven unprecedented performance and several benefits to extract information from chest X-ray data. This is one of the most common imaging exams, whose increasing demand is reflected in the aggravated radiologists’ workload. Consequently, healthcare would benefit from computer-aided diagnosis systems to prioritize certain exams and further identify possible pathologies. Pioneering work in chest X-ray analysis has focused on the identification of specific diseases, but to the best of the authors' knowledge no paper has specifically reviewed relevant work on abnormality detection and multi-label thoracic pathology classification. This paper focuses on those issues, selecting the leading chest X-ray based deep learning strategies for comparison. In addition, the paper discloses the current annotated public chest X-ray databases, covering the common thorax diseases.

Tuberculosis: Diagnostic Challenges in Rural Africa

Praxis ◽  
2019 ◽  
Vol 108 (15) ◽  
pp. 991-996
Author(s):  
Ngisi Masawa ◽  
Farida Bani ◽  
Robert Ndege
Keyword(s):  
Extrapulmonary Tuberculosis ◽  
X Ray ◽  
Molecular Tests ◽  
Rural Settings ◽  
Chest X Ray ◽  
Screening Questions ◽  
Rural Africa ◽  
Tb Diagnostics ◽  
Strip Test ◽  
Urine Strip

Abstract. Tuberculosis (TB) remains among the top 10 infectious diseases with highest mortality globally since the 1990s despite effective chemotherapy. Among 10 million patients that fell ill with tuberculosis in the year 2017, 36 % were undiagnosed or detected and not reported; the number goes as high as 55 % in Tanzania, showing that the diagnosis of TB is a big challenge in the developing countries. There have been great advancements in TB diagnostics with introduction of the molecular tests such as Xpert MTB/RIF, loop-mediated isothermal amplification, lipoarabinomannan urine strip test, and molecular line-probe assays. However, most of the hospitals in Tanzania still rely on the TB score chart in children, the WHO screening questions in adults, acid-fast bacilli and chest x-ray for the diagnosis of TB. Xpert MTB/RIF has been rolled-out but remains a challenge in settings where the samples for testing must be transported over many kilometers. Imaging by sonography – nowadays widely available even in rural settings of Tanzania – has been shown to be a useful tool in the diagnosis of extrapulmonary tuberculosis. Despite all the efforts and new diagnostics, 30–50 % of patients in high-burden TB countries are still empirically treated for tuberculosis. More efforts need to be placed if we are to reduce the death toll by 90 % until 2030.

Lingual Dystonia Treated with Botulinum Toxin - A Case Report

1970 ◽  
Vol 24 (2) ◽  
pp. 75-78
Author(s):  
MA Hayee ◽  
QD Mohammad ◽  
H Rahman ◽  
M Hakim ◽  
SM Kibria
Keyword(s):  
Botulinum Toxin ◽  
Botulinum Toxin A ◽  
Botulinum Toxin Type A ◽  
Botulinum Toxin Type ◽  
X Ray ◽  
Medical College ◽  
Lingual Dystonia ◽  
History Of ◽  
Chest X Ray

A 42-year-old female presented in Neurology Department of Sir Salimullah Medical College with gradually worsening difficulty in talking and eating for the last four months. Examination revealed dystonic tongue, macerated lips due to continuous drooling of saliva and aspirated lungs. She had no history of taking antiparkinsonian, neuroleptics or any other drugs causing dystonia. Chest X-ray revealed aspiration pneumonia corrected later by antibiotics. She was treated with botulinum toxin type-A. Twenty units of toxin was injected in six sites of the tongue. The dystonic tongue became normal by 24 hours. Subsequent 16 weeks follow up showed very good result and the patient now can talk and eat normally. (J Bangladesh Coll Phys Surg 2006; 24: 75-78)

Hubungan antara Luas Lesi pada Foto Toraks Penderita TB Paru Dewasa yang Memiliki Riwayat Diabetes Melitus dengan Indeks Massa Tubuh di Rumah Sakit Hasan Sadikin Bandung

2016 ◽  
Vol 1 (3) ◽  
pp. 138-144
Author(s):  
Ina Edwina ◽  
Rista D Soetikno ◽  
Irma H Hikmat
Keyword(s):  
Chest Radiograph ◽  
Chi Square ◽  
Pulmonary Tb ◽  
X Ray ◽  
Consecutive Sampling ◽  
Close Relationship ◽  
Chi Square Test ◽  
Chest X Ray ◽  
The Relationship ◽  
Diabetes Melitus

Background: Tuberculosis (TB) and diabetes mellitus (DM) prevalence rates are increasing rapidly, especially in developing countries like Indonesia. There is a relationship between TB and DM that are very prominent, which is the prevalence of pulmonary TB with DM increased by 20 times compared with pulmonary TB without diabetes. Chest X-ray picture of TB patients with DM is atypical lesion. However, there are contradictories of pulmonary TB lesion on chest radiograph of DM patients. Nutritional status has a close relationship with the morbidity of DM, as well as TB.Objectives: The purpose of this study was to determine the relationship between the lesions of TB on the chest radiograph of patients who su?er from DM with their Body Mass Index (BMI) in Hasan Sadikin Hospital Bandung.Material and Methods: The study was conducted in Department of Radiology RSHS Bandung between October 2014 - February 2015. We did a consecutive sampling of chest radiograph and IMT of DM patients with clinical diagnosis of TB, then the data was analysed by Chi Square test to determine the relationship between degree of lesions on chest radiograph of pulmonary TB on patients who have DM with their BMI.Results: The results showed that adult patients with active pulmonary TB with DM mostly in the range of age 51-70 years old, equal to 62.22%, with the highest gender in men, equal to 60%. Chest radiograph of TB in patients with DM are mostly seen in people who are obese, which is 40% and the vast majority of lesions are minimal lesions that is equal to 40%.Conclusions: There is a signifcant association between pulmonary TB lesion degree with BMI, with p = 0.03

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