COVID-19 Identification and Analysis with CT Scan Images using DenseNet and Support Vector Machine

Yu Jie Lim*, Kian Ming Lim, Chin Poo Lee, Roy Kwang Yang Chang, Jit Yan Lim

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

2 Citations (Scopus)

Abstract

Medical image analysis is the process of analyzing and interpreting medical images to diagnose diseases, assess disease progression, surgical planning and guide medical treatments by extracting clinically useful information from medical images. Medical image analysis serves an important role in applications in healthcare. With the advancement of deep learning techniques, the utilization of artificial intelligence for medical image analysis has experienced a notable surge, leading to improved accuracy and efficiency in diagnoses and treatment planning. In the present work, a pre-trained transfer learning model, DenseNet201 as a feature extractor, with a classifier of Support Vector Machine (SVM) is aimed to address the classification challenge associated with COVID-19 chest CT images. The evaluation of the proposed DenseNet201-SVM model has been conducted on three benchmark datasets: SARS-CoV-2 CT images, COVID-CT and Integrative CT images and CFs for COVID-19 (iCTCF) datasets and achieved accuracy of 98.99%, 93.33% and 99.25% respectively. The total number of images for each dataset are 2482, 746 and 19685. There are only two classes in first and second datasets, whereas the third dataset has three classes. The result is compared with other existing methods and the proposed DenseNet201-SVM model has outperformed other methods.

Original languageEnglish
Title of host publication2023 11th International Conference on Information and Communication Technology, ICoICT 2023
Pages254-259
Number of pages6
ISBN (Electronic)9798350321982
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event11th International Conference on Information and Communication Technology, ICoICT 2023 - Melaka, Malaysia
Duration: 23 Aug 202324 Aug 2023

Publication series

Name2023 11th International Conference on Information and Communication Technology, ICoICT 2023
Volume2023-August

Conference

Conference11th International Conference on Information and Communication Technology, ICoICT 2023
Country/TerritoryMalaysia
CityMelaka
Period23/08/2324/08/23

Keywords

  • COVID-19
  • CT-Scan
  • DenseNet
  • Medical Image Analysis
  • Support Vector Machine

Fingerprint

Dive into the research topics of 'COVID-19 Identification and Analysis with CT Scan Images using DenseNet and Support Vector Machine'. Together they form a unique fingerprint.

Cite this