Covidense: Providing a Suitable Solution for Diagnosing Covid-19 Lung Infection Based on Deep Learning from Chest X-Ray Images of Patients

Abstract

Purpose: Coronavirus disease 2019 (Covid-19), first reported in December 2019 in Wuhan, China, has become a pandemic. Chest imaging is used for the diagnosis of Covid-19 patients and can address problems concerning Reverse Transcription-Polymerase Chain Reaction (RT-PCR) shortcomings. Chest X-ray images can act as an appropriate alternative to Computed Tomography (CT) for diagnosing Covid-19. The purpose of this study is to use a Deep Learning method for diagnosing Covid-19 cases using chest X-ray images. Thus, we propose Covidense based on the pre-trained Densenet-201 model and is trained on a dataset comprising chest X-ray images of Covid-19, normal, bacterial pneumonia, and viral pneumonia cases.
 Materials and Methods: In this study, a total number of 1280 chest X-ray images of Covid-19, normal, bacterial and viral pneumonia cases were collected from open access repositories. Covidense, a convolutional neural network model, is based on the pre-trained DenseNet-201 architecture, and after pre-processing the images, it has been trained and tested on the images using the 5-fold cross-validation method.
 Results: The accuracy of different classifications including classification of two classes (Covid-19, normal), three classes 1 (Covid-19, normal and bacterial pneumonia), three classes 2 (Covid-19, normal and viral pneumonia), and four classes (Covid-19, normal, bacterial pneumonia and viral pneumonia) are 99.46%, 92.86%, 93.91 %, and 91.01% respectively.
 Conclusion: This model can differentiate pneumonia caused by Covid-19 from other types of pneumonia, including bacterial and viral. The proposed model offers high accuracy and can be of great help for effective screening. Thus, reducing the rate of infection spread. Also, it can act as a complementary tool for the detection and diagnosis of Covid-19.

Prediction of the Fatal Acute Complications of Myocardial Infarction via Machine Learning Algorithms

“…Sensitivity or Recall: It is the fraction of positive responses that have been correctly identified 9 , 16 : …”

“…Specificity: The ability of the test to correctly diagnose patients without the listed disease or condition 9 , 16 : …”

“…Precision (PPV): It indicates how close the measurement values are after consecutive value measurements 9 , 16 : …”

“…False-Positive Rate: it corresponds to the proportion of negative data points that are mistakenly considered as positive, with respect to all negative data points 9 , 16 : …”

“…Diagnosing COVID-19, chronic kidney disease, urinary infections, pulmonary hypertension, influenza, skin lesions, and acromegaly; predicting the risk of severe complications after bariatric surgery; 5 estimating the prevalence of long-term complications in patients with type 2 diabetes, 6 and determining the risk of major adverse events following intravenous lead extraction for cardiac rhythm management 7 are some examples of the application of ML in medicine. 8 , 9 Predicting fatal MI complications through prompt and critical preventive measures is crucial since competent experts are almost always unable to forecast all these problems.…”