Detection of leukocoria using a soft fusion of expert classifiers under non-clinical settings

Abstract

BackgroundLeukocoria 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.MethodsThis 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.ResultsThe 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.ConclusionsThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2415-14-110) contains supplementary material, which is available to authorized users.

Autonomous early detection of eye disease in childhood photographs

“…This study determined the longitudinal sensitivity, specificity, and accuracy of the application and, by retrospective analysis, whether the application could have detected leukocoria in a parent’s personal photographs before clinical diagnosis. Prior studies on the development of leukocoria detection algorithms ( 26 , 27 ) did not determine sensitivity, specificity, accuracy, or clinical timing of leukocoria detection in personal photographs. The current study also more firmly establishes the natural frequency of occurrence of physiologic and pathologic leukocoria in personal photographs collected throughout childhood (by parents).…”

“…However, photographic leukocoria can be difficult for parents to detect or recognize, especially when leukocoria appears at low intensity, low resolution, or in a small fraction of photographs ( 4 ). Artificial neural networks, operating autonomously on smartphones, might help parents detect leukocoria by routinely scanning personal photographs for leukocoria throughout development (as a supplement to clinical leukocoria screening) ( 26 ).…”

Accurate leukocoria predictor based on deep VGG‐net CNN technique

“…Rivas‐Perea et al [26] proposed a novel classification approach to detect leukocoria which is based on fuzzy logic with a number of classifiers. There are two transform approaches such as discrete cosine transform and Karhunen–Loeve transformation are used extract the features.…”

Determinants of late diagnosis and treatment of children with retinoblastoma. Analytical review of the organizational and methodological department of the Research Institute of Pediatric Oncology and Hematology at N.N. Blokhin National Medical Research Centre of Oncology, Ministry of Health of Russia

“…Самым частым симптомом, по которому примерно у одной трети пациентов находят РБ, является белое свечение зрачка (лейкокория), встречающееся у 70 % больных. Свечение возникает в результате отражения света от поверхности опухолевых масс светлых тонов серого или желтоватого цвета [8][9][10][11]. Острота зрения обоих глаз становится разной и поэтому у детей может появиться косоглазие (встречается примерно у 25-30 % заболевших).…”