Training set optimization and classifier performance in a top-down diabetic retinopathy screening system

J. Wigdahl, C. Agurto, V. Murray, S. Barriga, P. Soliz

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2 Citas (Scopus)


Diabetic retinopathy (DR) affects more than 4.4 million Americans age 40 and over. Automatic screening for DR has shown to be an efficient and cost-effective way to lower the burden on the healthcare system, by triaging diabetic patients and ensuring timely care for those presenting with DR. Several supervised algorithms have been developed to detect pathologies related to DR, but little work has been done in determining the size of the training set that optimizes an algorithm's performance. In this paper we analyze the effect of the training sample size on the performance of a top-down DR screening algorithm for different types of statistical classifiers. Results are based on partial least squares (PLS), support vector machines (SVM), k-nearest neighbor (kNN), and Naïve Bayes classifiers. Our dataset consisted of digital retinal images collected from a total of 745 cases (595 controls, 150 with DR). We varied the number of normal controls in the training set, while keeping the number of DR samples constant, and repeated the procedure 10 times using randomized training sets to avoid bias. Results show increasing performance in terms of area under the ROC curve (AUC) when the number of DR subjects in the training set increased, with similar trends for each of the classifiers. Of these, PLS and k-NN had the highest average AUC. Lower standard deviation and a flattening of the AUC curve gives evidence that there is a limit to the learning ability of the classifiers and an optimal number of cases to train on.

Idioma originalInglés
Título de la publicación alojadaMedical Imaging 2013
Subtítulo de la publicación alojadaComputer-Aided Diagnosis
EstadoPublicada - 2013
Publicado de forma externa
EventoMedical Imaging 2013: Computer-Aided Diagnosis - Lake Buena Vista, FL, Estados Unidos
Duración: 12 feb. 201314 feb. 2013

Serie de la publicación

NombreProceedings of SPIE - The International Society for Optical Engineering
ISSN (versión impresa)0277-786X


ConferenciaMedical Imaging 2013: Computer-Aided Diagnosis
País/TerritorioEstados Unidos
CiudadLake Buena Vista, FL


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