Journal
APPLIED SCIENCES-BASEL
Volume 11, Issue 23, Pages -Publisher
MDPI
DOI: 10.3390/app112311423
Keywords
chest X-rays; COVID-19; pneumonia; transfer learning; fuzzy ensemble; GoogleNet; Xception; Majority Voting; Sugeno Integral; Choquet Fuzzy
Ask authors/readers for more resources
This study used chest X-ray images to identify COVID-19 patients and proposed a method that combined fuzzy ensemble techniques, outperforming other techniques and ensemble methods with higher accuracy.
The COVID-19 pandemic has claimed the lives of millions of people and put a significant strain on healthcare facilities. To combat this disease, it is necessary to monitor affected patients in a timely and cost-effective manner. In this work, CXR images were used to identify COVID-19 patients. We compiled a CXR dataset with equal number of 2313 COVID positive, pneumonia and normal CXR images and utilized various transfer learning models as base classifiers, including VGG16, GoogleNet, and Xception. The proposed methodology combines fuzzy ensemble techniques, such as Majority Voting, Sugeno Integral, and Choquet Fuzzy, and adaptively combines the decision scores of the transfer learning models to identify coronavirus infection from CXR images. The proposed fuzzy ensemble methods outperformed each individual transfer learning technique and several state-of-the-art ensemble techniques in terms of accuracy and prediction. Specifically, VGG16 + Choquet Fuzzy, GoogleNet + Choquet Fuzzy, and Xception + Choquet Fuzzy achieved accuracies of 97.04%, 98.48%, and 99.57%, respectively. The results of this work are intended to help medical practitioners achieve an earlier detection of coronavirus compared to other detection strategies, which can further save millions of lives and advantageously influence society.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available