Your browser doesn't support javascript.
Detection of COVID-19 Infection in CT and X-ray images using transfer learning approach.
Tiwari, Alok; Tripathi, Sumit; Pandey, Dinesh Chandra; Sharma, Neeraj; Sharma, Shiru.
  • Tiwari A; School of Biomedical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, India.
  • Tripathi S; School of Biomedical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, India.
  • Pandey DC; Department of Electronics and Communication Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India.
  • Sharma N; School of Biomedical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, India.
  • Sharma S; Department of Management Studies, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India.
Technol Health Care ; 30(6): 1273-1286, 2022.
Article in English | MEDLINE | ID: covidwho-2119015
ABSTRACT

BACKGROUND:

The infection caused by the SARS-CoV-2 (COVID-19) pandemic is a threat to human lives. An early and accurate diagnosis is necessary for treatment.

OBJECTIVE:

The study presents an efficient classification methodology for precise identification of infection caused by COVID-19 using CT and X-ray images.

METHODS:

The depthwise separable convolution-based model of MobileNet V2 was exploited for feature extraction. The features of infection were supplied to the SVM classifier for training which produced accurate classification results.

RESULT:

The accuracies for CT and X-ray images are 99.42% and 98.54% respectively. The MCC score was used to avoid any mislead caused by accuracy and F1 score as it is more mathematically balanced metric. The MCC scores obtained for CT and X-ray were 0.9852 and 0.9657, respectively. The Youden's index showed a significant improvement of more than 2% for both imaging techniques.

CONCLUSION:

The proposed transfer learning-based approach obtained the best results for all evaluation metrics and produced reliable results for the accurate identification of COVID-19 symptoms. This study can help in reducing the time in diagnosis of the infection.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: Deep Learning / COVID-19 Type of study: Experimental Studies / Prognostic study Limits: Humans Language: English Journal: Technol Health Care Journal subject: Biomedical Engineering / Health Services Year: 2022 Document Type: Article Affiliation country: THC-220114

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Deep Learning / COVID-19 Type of study: Experimental Studies / Prognostic study Limits: Humans Language: English Journal: Technol Health Care Journal subject: Biomedical Engineering / Health Services Year: 2022 Document Type: Article Affiliation country: THC-220114