title: Detection of leukocoria using a soft fusion of expert classifiers under non-clinical settings.
abstract: BACKGROUND
  Leukocoria is defined as a white reflection and its manifestation is symptomatic of several ocular pathologies, including retinoblastoma (Rb). Early detection of recurrent leukocoria is critical for improved patient outcomes and can be accomplished via the examination of recreational photography. To date, there exists a paucity of methods to automate leukocoria detection within such a dataset.
  
  
  METHODS
  This research explores a novel classification scheme that uses fuzzy logic theory to combine a number of classifiers that are experts in performing multichannel detection of leukocoria from recreational photography. The proposed scheme extracts features aided by the discrete cosine transform and the Karhunen-Loeve transformation.
  
  
  RESULTS
  The soft fusion of classifiers is significantly better than other methods of combining classifiers with p = 1.12 × 10-5. The proposed methodology performs at a 92% accuracy rate, with an 89% true positive rate, and an 11% false positive rate. Furthermore, the results produced by our methodology exhibit the lowest average variance.
  
  
  CONCLUSIONS
  The proposed methodology overcomes non-ideal conditions of image acquisition, presenting a competent approach for the detection of leukocoria. Results suggest that recreational photography can be used in combination with the fusion of individual experts in multichannel classification and preprocessing tools such as the discrete cosine transform and the Karhunen-Loeve transformation.
authors.affiliation: Department of Computer Science, Baylor University, One Bear Place #97356, Waco, TX 76798-7356, USA. Pablo_Rivas_Perea@Baylor.edu.
authors.forename: Pablo
authors.initials: P
authors.lastname: Rivas-Perea
authors.forename: Erich
authors.initials: E
authors.lastname: Baker
authors.forename: Greg
authors.initials: G
authors.lastname: Hamerly
authors.forename: Bryan F
authors.initials: BF
authors.lastname: Shaw
mesh_terms: D000465:Algorithms; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000379:methods*; D011681:Pupil Disorders / Q000175:diagnosis*; D015203:Reproducibility of Results