Your browser doesn't support javascript.
Detection and analysis of COVID-19 in medical images using deep learning techniques.
Yang, Dandi; Martinez, Cristhian; Visuña, Lara; Khandhar, Hardev; Bhatt, Chintan; Carretero, Jesus.
  • Yang D; Beijing Electro-Mechanical Engineering Institute, Beijing, 100074, China.
  • Martinez C; Department of Computer Science and Engineering, Carlos III University of Madrid, 28911, Madrid, Spain.
  • Visuña L; Department of Computer Science and Engineering, Carlos III University of Madrid, 28911, Madrid, Spain.
  • Khandhar H; U & P U. Patel Department of Computer Engineering, CSPIT, Charotar University of Science and Technology (CHARUSAT), Changa, India.
  • Bhatt C; U & P U. Patel Department of Computer Engineering, CSPIT, Charotar University of Science and Technology (CHARUSAT), Changa, India.
  • Carretero J; Department of Computer Science and Engineering, Carlos III University of Madrid, 28911, Madrid, Spain. jesus.carretero@uc3m.es.
Sci Rep ; 11(1): 19638, 2021 10 04.
Article in English | MEDLINE | ID: covidwho-1450291
ABSTRACT
The main purpose of this work is to investigate and compare several deep learning enhanced techniques applied to X-ray and CT-scan medical images for the detection of COVID-19. In this paper, we used four powerful pre-trained CNN models, VGG16, DenseNet121, ResNet50,and ResNet152, for the COVID-19 CT-scan binary classification task. The proposed Fast.AI ResNet framework was designed to find out the best architecture, pre-processing, and training parameters for the models largely automatically. The accuracy and F1-score were both above 96% in the diagnosis of COVID-19 using CT-scan images. In addition, we applied transfer learning techniques to overcome the insufficient data and to improve the training time. The binary and multi-class classification of X-ray images tasks were performed by utilizing enhanced VGG16 deep transfer learning architecture. High accuracy of 99% was achieved by enhanced VGG16 in the detection of X-ray images from COVID-19 and pneumonia. The accuracy and validity of the algorithms were assessed on X-ray and CT-scan well-known public datasets. The proposed methods have better results for COVID-19 diagnosis than other related in literature. In our opinion, our work can help virologists and radiologists to make a better and faster diagnosis in the struggle against the outbreak of COVID-19.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Deep Learning / COVID-19 Type of study: Diagnostic study / Prognostic study Limits: Humans Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-99015-3

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Deep Learning / COVID-19 Type of study: Diagnostic study / Prognostic study Limits: Humans Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-99015-3