Selected article for: "exponential growth and infected people"
Author: Kedia, Priyansh; Anjum; Katarya, Rahul
Title: CoVNet-19: A Deep Learning model for the detection and analysis of COVID-19 patients
  • Cord-id: ua88h2ds
  • Document date: 2021_2_15
    • ID: ua88h2ds
    Snippet: BACKGROUND: The ongoing fight with Novel Corona Virus, getting quick treatment, and rapid diagnosis reports have become an act of high priority. With millions getting infected daily and a fatality rate of 2%, we made it our motive to contribute a little to solve this real-world problem by accomplishing a significant and substantial method for diagnosing COVID-19 patients. AIM: The Exponential growth of COVID-19 cases worldwide has severely affected the health care system of highly populated coun
    Document: BACKGROUND: The ongoing fight with Novel Corona Virus, getting quick treatment, and rapid diagnosis reports have become an act of high priority. With millions getting infected daily and a fatality rate of 2%, we made it our motive to contribute a little to solve this real-world problem by accomplishing a significant and substantial method for diagnosing COVID-19 patients. AIM: The Exponential growth of COVID-19 cases worldwide has severely affected the health care system of highly populated countries due to proportionally a smaller number of medical practitioners, testing kits, and other resources, thus becoming essential to identify the infected people. Catering to the above problems, the purpose of this paper is to formulate an accurate, efficient, and time-saving method for detecting positive corona patients. METHOD: In this paper, an Ensemble Deep Convolution Neural Network model “CoVNet-19” is being proposed that can unveil important diagnostic characteristics to find COVID-19 infected patients using X-ray images chest and help radiologists and medical experts to fight this pandemic. RESULTS: The experimental results clearly show that the overall classification accuracy obtained with the proposed approach for three-class classification among COVID-19, Pneumonia, and Normal is 98.28%, along with an average precision and Recall of 98.33% and 98.33%, respectively. Besides this, for binary classification between Non-COVID and COVID Chest X-ray images, an overall accuracy of 99.71% was obtained. CONCLUSION: Having a high diagnostic accuracy, our proposed ensemble Deep Learning classification model can be a productive and substantial contribution to detecting COVID-19 infected patients.

    Search related documents:
    Co phrase search for related documents
    • accuracy achieve and achieve mcc: 1, 2
    • accuracy achieve and achieve test set accuracy: 1, 2, 3, 4
    • accuracy achieve and achieve validation training accuracy: 1, 2
    • accuracy achieve and acute respiratory: 1, 2, 3, 4, 5, 6, 7
    • accuracy auc achieve and acute respiratory: 1
    • accuracy auc and activation mapping: 1
    • accuracy auc and acute respiratory: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19
    • accuracy rate and achieve test set accuracy: 1
    • accuracy rate and acute respiratory: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
    • accuracy recall and achieve test set accuracy: 1
    • accuracy recall and activation mapping: 1, 2, 3
    • accuracy recall and acute respiratory: 1, 2, 3, 4
    • accuracy recall precision and achieve test set accuracy: 1
    • accuracy recall precision and activation mapping: 1, 2, 3
    • accuracy recall precision and acute respiratory: 1, 2, 3
    • accurate fast and activation mapping: 1
    • accurate fast and acute respiratory: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24
    • activation map and acute respiratory: 1, 2