A fundamental study assessing the generalized fitting method in conjunction with every possible coalition of N-combinations (G-EPOC) using the appendicitis detection task of computed tomography

Abstract

PURPOSE: Increased use of deep learning (DL) in medical imaging diagnoses has led to more frequent use of 10-fold cross-validation (10-CV) for the evaluation of the performance of DL. To eliminate some of the (10-fold) repetitive processing in 10-CV, we proposed a "generalized fitting method in conjunction with every possible coalition of N-combinations (G-EPOC)", to estimate the range of the mean accuracy of 10-CV using less than 10 results of 10-CV. MATERIAL AND METHODS: G-EPOC was executed as follows. We first provided (2N-1) coalition subsets using a specified N, which was 9 or less, out of 10 result datasets of 10-CV. We then obtained the estimation range of the accuracy by applying those subsets to the distribution fitting twice using a combination of normal, binominal, or Poisson distributions. Using datasets of 10-CVs acquired from the practical detection task of the appendicitis on CT by DL, we scored the estimation success rates if the range provided by G-EPOC included the true accuracy. RESULTS: G-EPOC successfully estimated the range of the mean accuracy by 10-CV at over 95% rates for datasets with N assigned as 2 to 9. CONCLUSIONS: G-EPOC will help lessen the consumption of time and computer resources in the development of computerbased diagnoses in medical imaging and could become an option for the selection of a reasonable K value in K-CV.

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