scholarly journals Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images

2021 ◽  
Vol 7 (1) ◽  
pp. 7
Author(s):  
Sami Bourouis ◽  
Abdullah Alharbi ◽  
Nizar Bouguila
Keyword(s):  
Mixture Models ◽  
Bayesian Learning ◽  
Bayesian Framework ◽  
Model Parameters ◽  
X Ray ◽  
Therapeutic Implications ◽  
Biomedical Image ◽  
Dirichlet Mixture ◽  
Chest X Ray ◽  
Fully Bayesian

Early diagnosis and assessment of fatal diseases and acute infections on chest X-ray (CXR) imaging may have important therapeutic implications and reduce mortality. In fact, many respiratory diseases have a serious impact on the health and lives of people. However, certain types of infections may include high variations in terms of contrast, size and shape which impose a real challenge on classification process. This paper introduces a new statistical framework to discriminate patients who are either negative or positive for certain kinds of virus and pneumonia. We tackle the current problem via a fully Bayesian approach based on a flexible statistical model named shifted-scaled Dirichlet mixture models (SSDMM). This mixture model is encouraged by its effectiveness and robustness recently obtained in various image processing applications. Unlike frequentist learning methods, our developed Bayesian framework has the advantage of taking into account the uncertainty to accurately estimate the model parameters as well as the ability to solve the problem of overfitting. We investigate here a Markov Chain Monte Carlo (MCMC) estimator, which is a computer–driven sampling method, for learning the developed model. The current work shows excellent results when dealing with the challenging problem of biomedical image classification. Indeed, extensive experiments have been carried out on real datasets and the results prove the merits of our Bayesian framework.

scholarly journals A Hybrid Capsule Network for Pneumonia Detection Using Image Augmentation Based on Generative Adversarial Network

2021 ◽  
Vol 38 (3) ◽  
pp. 619-627
Author(s):  
Kazim Firildak ◽  
Muhammed Fatih Talu
Keyword(s):  
Classification Accuracy ◽  
Data Augmentation ◽  
Model Parameters ◽  
Classification Problems ◽  
Generative Adversarial Network ◽  
X Ray ◽  
Discrimination Capability ◽  
Adversarial Network ◽  
Original Dataset ◽  
Chest X Ray

Pneumonia, featured by inflammation of the air sacs in one or both lungs, is usually detected by examining chest X-ray images. This paper probes into the classification models that can distinguish between normal and pneumonia images. As is known, trained networks like AlexNet and GoogleNet are deep network architectures, which are widely adopted to solve many classification problems. They have been adapted to the target datasets, and employed to classify new data generated through transfer learning. However, the classical architectures are not accurate enough for the diagnosis of pneumonia. Therefore, this paper designs a capsule network with high discrimination capability, and trains the network on Kaggle’s online pneumonia dataset, which contains chest X-ray images of many adults and children. The original dataset consists of 1,583 normal images, and 4,273 pneumonia images. Then, two data augmentation approaches were applied to the dataset, and their effects on classification accuracy were compared in details. The model parameters were optimized through five different experiments. The results show that the highest classification accuracy (93.91% even on small images) was achieved by the capsule network, coupled with data augmentation by generative adversarial network (GAN), using optimized parameters. This network outperformed the classical strategies.

scholarly journals Covid-19 News Clustering using MCMC-Based Learing of finite EMSD Mixture Models

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Xuanbo Su ◽  
Nizar Bouguila ◽  
Nuha Zamzami
Keyword(s):  
Mixture Models ◽  
Data Clustering ◽  
Bayesian Learning ◽  
Finite Mixture Models ◽  
State Of The Art ◽  
Generative Models ◽  
Finite Mixture ◽  
Model Parameters ◽  
Different Types ◽  
Statistical Approaches

With the growth of social media information on the Web, performing clustering on different types of data is a challenging task.Statistical approaches are widely used to tackle this task. Among the successful statistical approaches, finite mixture models have received a lot attention thanks to their flexibility. There are already many finite mixture models to cope with this task, but the Exponential Multinomial Scaled Dirichlet Distributions (EMSD) has recently shown to attain higher accuracy compared to other state-of-the-art generative models for count data clustering. Thus, in this paper, we present a Bayesian learning method based on Markov Chain Monte Carlo and Metropolis-Hastings algorithm for learning this model parameters. This proposed method is validated via extensive simulations and comparison with multinomial based mixture models.

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

Chest X-Ray Image Classification on Common Thorax Diseases using GLCM and AlexNet Deep Features

2019 ◽  
Vol 11 (4) ◽  
Author(s):  
Tengku Afiah Mardhiah Tengku Zainul Akmal ◽  
Joel Chia Ming Than ◽  
Haslailee Abdullah ◽  
Norliza Mohd Noor
Keyword(s):  
Image Classification ◽  
X Ray ◽  
Chest X Ray
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