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The use of Convolutional Neural Networks (CNN) in natural imageclassification systems has produced very impressive results. Combined with theinherent nature of medical images that make them ideal for deep-learning,further application of such systems to medical image classification holds muchpromise. However, the usefulness and potential impact of such a system can becompletely negated if it does not reach a target accuracy. In this paper, wepresent a study on determining the optimum size of the training data setnecessary to achieve high classification accuracy with low variance in medicalimage classification systems. The CNN was applied to classify axial ComputedTomography (CT) images into six anatomical classes. We trained the CNN usingsix different sizes of training data set (5, 10, 20, 50, 100, and 200) and thentested the resulting system with a total of 6000 CT images. All images wereacquired from the Massachusetts General Hospital (MGH) Picture Archiving andCommunication System (PACS). Using this data, we employ the learning curveapproach to predict classification accuracy at a given training sample size.Our research will present a general methodology for determining the trainingdata set size necessary to achieve a certain target classification accuracythat can be easily applied to other problems within such systems.

How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?