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The aim of this paper is to increase the accuracy of an architecture, a convolutional neural network in the TensorFlow machine learning library, in a scenario where training data is limited and small. By pre-processing the image data using color quantization with the help of K-means clustering, and histogram equalization. It is expected that the prediction accuracy will increase more than the raw training data in the usage of both pre-processing methods.
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