Author: Pathak, Yadunath; Shukla, Prashant Kumar; Tiwari, Akhilesh; Stalin, Shalini; Singh, Saurabh; Shukla, Piyush Kumar
Title: Deep Transfer Learning based Classification Model for COVID-19 Disease Cord-id: oegn8m1k Document date: 2020_5_20
ID: oegn8m1k
Snippet: Abstract The COVID-19 infection is increasing at a rapid rate, with the availability of limited number of testing kits. Therefore, the development of COVID-19 testing kits is still an open area of research. Recently, many studies have shown that chest Computed Tomography(CT) images can be used for COVID-19 testing, as chest CT images show a bilateral change in COVID-19 infected patients. However, the classification of COVID-19 patients from chest CT images is not an easy task as predicting the b
Document: Abstract The COVID-19 infection is increasing at a rapid rate, with the availability of limited number of testing kits. Therefore, the development of COVID-19 testing kits is still an open area of research. Recently, many studies have shown that chest Computed Tomography(CT) images can be used for COVID-19 testing, as chest CT images show a bilateral change in COVID-19 infected patients. However, the classification of COVID-19 patients from chest CT images is not an easy task as predicting the bilateral change is defined as an ill-posed problem. Therefore, in this paper, a deep transfer learning technique is used to classify COVID-19 infected patients. Additionally, a top-2 smooth loss function with cost-sensitive attributes is also utilized to handle noisy and imbalanced COVID-19 dataset kind of problems. Experimental results reveal that the proposed deep transfer learning-based COVID-19 classification model provides efficient results as compared to the other supervised learning models.
Search related documents:
Co phrase search for related documents- activation function and machine learning: 1, 2, 3, 4, 5, 6
- activation function and machine learning model: 1
- loss function and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23
- loss function and machine learning model: 1, 2, 3
- loss function and machine learning technique: 1
- low sensitivity and lung opacity: 1
- low sensitivity and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21
- low sensitivity and machine learning model: 1, 2, 3, 4
- low sensitivity and machine learning technique: 1
- lung opacity and machine learning: 1, 2, 3, 4, 5, 6
Co phrase search for related documents, hyperlinks ordered by date