scholarly journals Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic

2021 ◽  
Author(s):  
Sameh H. Basha ◽  
Ahmed M. Anter ◽  
Aboul Ella Hassanien ◽  
Areeg Abdalla
Keyword(s):  
Genetic Algorithm ◽  
X Ray ◽  
Neutrosophic Logic ◽  
Chest X Ray ◽  
Intelligent Model

scholarly journals Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242899
Author(s):  
Musatafa Abbas Abbood Albadr ◽  
Sabrina Tiun ◽  
Masri Ayob ◽  
Fahad Taha AL-Dhief ◽  
Khairuddin Omar ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Ct Scan ◽  
Extreme Learning Machine ◽  
Experimental Result ◽  
Support Vector ◽  
X Ray ◽  
Chest X Ray ◽  
Learning Machine

The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) high capability of the ELM in avoiding overfitting; (ii) its usability on binary and multi-type classifiers; and (iii) ELM could work as a kernel-based support vector machine with a structure of a neural network. These advantages make the ELM efficient in achieving an excellent learning performance. ELMs have successfully been applied in many domains, including medical domains such as breast cancer detection, pathological brain detection, and ductal carcinoma in situ detection, but not yet tested on detecting COVID-19. Hence, this work aims to identify the effectiveness of employing OGA-ELM in detecting COVID-19 using chest X-ray images. In order to reduce the dimensionality of a histogram oriented gradient features, we use principal component analysis. The performance of OGA-ELM is evaluated on a benchmark dataset containing 188 chest X-ray images with two classes: a healthy and a COVID-19 infected. The experimental result shows that the OGA-ELM achieves 100.00% accuracy with fast computation time. This demonstrates that OGA-ELM is an efficient method for COVID-19 detecting using chest X-ray images.

scholarly journals Multiobjective Genetic Algorithm and Convolutional Neural Network Based COVID-19 Identification in Chest X-Ray Images

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Prashant Kumar Shukla ◽  
Jasminder Kaur Sandhu ◽  
Anamika Ahirwar ◽  
Deepika Ghai ◽  
Priti Maheshwary ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Diagnostic Testing ◽  
The Novel ◽  
Screening Process ◽  
X Ray ◽  
Multiobjective Genetic Algorithm ◽  
Global Pandemic ◽  
Chest X Ray ◽  
Novel Coronavirus ◽  
Fold Cross Validation

COVID-19 is a new disease, caused by the novel coronavirus SARS-CoV-2, that was firstly delineated in humans in 2019.Coronaviruses cause a range of illness in patients varying from common cold to advanced respiratory syndromes such as Severe Acute Respiratory Syndrome (SARS-CoV) and Middle East Respiratory Syndrome (MERS-CoV). The SARS-CoV-2 outbreak has resulted in a global pandemic, and its transmission is increasing at a rapid rate. Diagnostic testing and approaches provide a valuable tool for doctors and support them with the screening process. Automatic COVID-19 identification in chest X-ray images can be useful to test for COVID-19 infection at a good speed. Therefore, in this paper, a framework is designed by using Convolutional Neural Networks (CNN) to diagnose COVID-19 patients using chest X-ray images. A pretrained GoogLeNet is utilized for implementing the transfer learning (i.e., by replacing some sets of final network CNN layers). 20-fold cross-validation is considered to overcome the overfitting quandary. Finally, the multiobjective genetic algorithm is considered to tune the hyperparameters of the proposed COVID-19 identification in chest X-ray images. Extensive experiments show that the proposed COVID-19 identification model obtains remarkably better results and may be utilized for real-time testing of patients.

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)

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