Mining x-ray images of SARS patients

  • Authors:

  • Xuanyang Xie;Xi Li;Shouhong Wan;Yuchang Gong

  • Affiliations:

  • Department of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China;Department of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China;Department of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China;Department of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China

  • Venue:
  • Data Mining
  • Year:
  • 2006

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Abstract

Severe Acute Respiratory Syndrome (SARS) has infected more than 8,000 persons [1] after it first broke out in Guangdong China. As there was no fast and effective detection method of suspected SARS cases,this paper proposes a computer aided SARS detection system (CADSARS) based on data mining techniques.‘Typical pneumonia’ and SARS X-Ray chest radiographs were collected.Feature extraction of these images was performed after segmenting out pulmonary fields. Feature vectors were then constructed to build rules for the discrimination of SARS and ‘typical pneumonia’.Three methods were used to classify these images: C4.5, neural network and CART.Final results show that about 70.94% SARS cases can be detected. ROC charts and confusion matrix by these three methods are given and analyzed.Association rules mining was used to find whether there exists difference of lesions’ location between SARS and pneumonia cases.