title: Detecting Lung Diseases from Exhaled Aerosols: Non-Invasive Lung Diagnosis Using Fractal Analysis and SVM Classification.
abstract: BACKGROUND
  Each lung structure exhales a unique pattern of aerosols, which can be used to detect and monitor lung diseases non-invasively. The challenges are accurately interpreting the exhaled aerosol fingerprints and quantitatively correlating them to the lung diseases.
  
  
  OBJECTIVE
  In this study, we presented a paradigm of an exhaled aerosol test that addresses the above two challenges and is promising to detect the site and severity of lung diseases. This paradigm consists of two steps: image feature extraction using sub-regional fractal analysis and data classification using a support vector machine (SVM). Numerical experiments were conducted to evaluate the feasibility of the breath test in four asthmatic lung models. A high-fidelity image-CFD approach was employed to compute the exhaled aerosol patterns under different disease conditions.
  
  
  RESULTS
  By employing the 10-fold cross-validation method, we achieved 100% classification accuracy among four asthmatic models using an ideal 108-sample dataset and 99.1% accuracy using a more realistic 324-sample dataset. The fractal-SVM classifier has been shown to be robust, highly sensitive to structural variations, and inherently suitable for investigating aerosol-disease correlations.
  
  
  CONCLUSIONS
  For the first time, this study quantitatively linked the exhaled aerosol patterns with their underlying diseases and set the stage for the development of a computer-aided diagnostic system for non-invasive detection of obstructive respiratory diseases.
authors.affiliation: School of Engineering and Technology, Central Michigan University, Mount Pleasant, Michigan, United States of America.
authors.forename: Jinxiang
authors.initials: J
authors.lastname: Xi
authors.affiliation: College of Information Engineering, Xiangtan University, Xiangtan, Hunan Province, China; Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, Jefferson, Arkansas, United States of America.
authors.forename: Weizhong
authors.initials: W
authors.lastname: Zhao
authors.affiliation: School of Engineering and Technology, Central Michigan University, Mount Pleasant, Michigan, United States of America.
authors.forename: Jiayao Eddie
authors.initials: JE
authors.lastname: Yuan
authors.affiliation: College of Engineering, University of Georgia, Athens, Georgia, United States of America.
authors.forename: JongWon
authors.initials: J
authors.lastname: Kim
authors.affiliation: Department of Mechanical Engineering, California Baptist University, Riverside, California, United States of America.
authors.forename: Xiuhua
authors.initials: X
authors.lastname: Si
authors.affiliation: Department of Information Science, University of Arkansas, Little Rock, Arkansas, United States of America.
authors.forename: Xiaowei
authors.initials: X
authors.lastname: Xu
mesh_terms: D000336:Aerosols / Q000737:chemistry* / Q000145:classification; D001249:Asthma / Q000175:diagnosis*; D001944:Breath Tests; D006801:Humans; D008171:Lung Diseases / Q000175:diagnosis*; D011379:Prognosis; D060388:Support Vector Machine
chemical_list: D000336:Aerosols