Effects of sample size on classifier design for computer-aided diagnosis

Heang-Ping Chan; Berkman Sahiner; Robert F. Wagner; Nicholas Petrick

doi:10.1117/12.310895

24 June 1998 Effects of sample size on classifier design for computer-aided diagnosis

Heang-Ping Chan, Berkman Sahiner, Robert F. Wagner, Nicholas Petrick

Proceedings Volume 3338, Medical Imaging 1998: Image Processing; (1998) https://doi.org/10.1117/12.310895
Event: Medical Imaging '98, 1998, San Diego, CA, United States

Abstract

One of the important issues in the development of computer- aided diagnosis (CAD) algorithms is the design of classifiers. A classifier is designed with case samples drawn from the patient population. Generally, the sample size available for classifier design is limited, which introduces bias and variance into the performance of the trained classifier. Fukunaga showed that the bias on the probability of misclassification is proportional to 1/N_t, where N_t is the design (training) sample size, under conditions that the higher-order terms can be neglected. For CAD applications, a commonly used performance index for a classifier is the area, A_z, under the receiver operating characteristic curve. We have studied the dependence of the bias in A_z on sample size by computer simulation for a linear classifier and nonlinear classifiers such as the quadratic and the backpropagation neural network classifiers.

Citation Download Citation

Heang-Ping Chan, Berkman Sahiner, Robert F. Wagner, and Nicholas Petrick "Effects of sample size on classifier design for computer-aided diagnosis", Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); https://doi.org/10.1117/12.310895

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